GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-9-2809-2016LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil
moisture Model Intercomparison Project – aims, setup and expected outcomevan den HurkBarthurkvd@knmi.nlhttps://orcid.org/0000-0003-3726-7086KimHyungjunhttps://orcid.org/0000-0003-1083-8416KrinnerGerhardSeneviratneSonia I.DerksenChrisOkiTaikanDouvilleHervéColinJeanneDucharneAgnèsCheruyFrederiqueViovyNicholasPumaMichael J.WadaYoshihidehttps://orcid.org/0000-0003-4770-2539LiWeipingJiaBinghaohttps://orcid.org/0000-0002-9354-0457AlessandriAndreaLawrenceDave M.https://orcid.org/0000-0002-2968-3023WeedonGraham P.https://orcid.org/0000-0003-1262-9984EllisRichardHagemannStefanMaoJiafuFlannerMark G.ZampieriMatteohttps://orcid.org/0000-0002-7558-1108MateriaStefanoLawRachel M.https://orcid.org/0000-0002-7346-0927SheffieldJustinhttps://orcid.org/0000-0003-2400-0630KNMI, De Bilt, the NetherlandsInstitute of Industrial Science, the University of Tokyo, Tokyo, JapanLGGE, CNRS, Grenoble, FranceInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandClimate Research Division, Environment and Climate Change, Toronto, CanadaCNRM, Centre National de Recherches Météorologiques, Météo-France, Toulouse, FranceLMD-IPSL, Centre National de la Recherche Scientifique, Université Pierre et Marie-Curie, Ecole Normale Supérieure, Ecole Polytechnique, Paris, FranceLSCE-IPSL: CEA-CNRS-UVSQ, Gif-sur-Yvette, FranceNASA Goddard Institute for Space Studies and Center for Climate Systems Research, Columbia University, New York, USAInternational Institute for Applied Systems Analysis, Laxenburg, AustriaLaboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, ChinaState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, ChinaAgenzia Nazionale per le nuove Tecnologie, l'energia e lo sviluppo economico sostenibile, Rome, ItalyClimate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, USAMet Office (JCHMR) Maclean Building Crowmarsh Gifford Wallingford, Oxfordshire, UKCentre for Ecology and Hydrology, Maclean Building Crowmarsh Gifford Wallingford, Oxfordshire, UKMax-Planck-Institut für Meteorologie, Hamburg, GermanyEnvironmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USADepartment of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, USAEuro-Mediterranean Center for Climate Change (CMCC), Climate Simulation and Prediction Division, Bologna, ItalyCSIRO Oceans and Atmosphere, Aspendale, AustraliaDepartment of Civil and Environmental Engineering Princeton University, Princeton, USAGeography and Environment, University of Southampton, Southampton, UKSorbonne Universités, UMR 7619 METIS, UPMC/CNRS/EPHE, Paris, FranceBart van den Hurk (hurkvd@knmi.nl)24August2016982809283230March201611April201627July201628July2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://gmd.copernicus.org/articles/9/2809/2016/gmd-9-2809-2016.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/9/2809/2016/gmd-9-2809-2016.pdf
The Land Surface, Snow and Soil Moisture Model Intercomparison Project
(LS3MIP) is designed to provide a comprehensive assessment of land surface,
snow and soil moisture feedbacks on climate variability and climate change,
and to diagnose systematic biases in the land modules of current Earth
system models (ESMs). The solid and liquid water stored at the land surface
has a large influence on the regional climate, its variability and
predictability, including effects on the energy, water and carbon cycles.
Notably, snow and soil moisture affect surface radiation and flux
partitioning properties, moisture storage and land surface memory. They both
strongly affect atmospheric conditions, in particular surface air
temperature and precipitation, but also large-scale circulation patterns.
However, models show divergent responses and representations of these
feedbacks as well as systematic biases in the underlying processes. LS3MIP
will provide the means to quantify the associated uncertainties and better
constrain climate change projections, which is of particular interest for
highly vulnerable regions (densely populated areas, agricultural regions,
the Arctic, semi-arid and other sensitive terrestrial ecosystems).
The experiments are subdivided in two components, the first addressing
systematic land biases in offline mode (“LMIP”, building upon the 3rd
phase of Global Soil Wetness Project; GSWP3) and the second addressing land
feedbacks attributed to soil moisture and snow in an integrated framework
(“LFMIP”, building upon the GLACE-CMIP blueprint).
Introduction
Land surface processes, including heat fluxes, snow, soil moisture,
vegetation, turbulent transfer and runoff, continue to be ranked highly on
the list of the most relevant yet complex and poorly represented features in
state-of-the-art climate models. People live on land, exploit its water and
natural resources and experience day-to-day weather that is strongly
affected by feedbacks with the land surface. The six Grand Challenges of the
World Climate Research Program (WCRP)
http://www.wcrp-climate.org/grand-challenges
include topics governed
primarily (Water Availability, Cryosphere) or largely (Climate Extremes) by
land surface characteristics.
Despite the importance of a credible representation of land surface
processes in Earth system models (ESMs), a number of systematic biases and
uncertainties persist. Biases in hydrological characteristics (e.g., moisture
storage in soil and snow, runoff, vegetation and surface water bodies),
partitioning of energy and water fluxes (Seneviratne et
al., 2010), definition of initial and boundary conditions at the appropriate
spatial scale, feedback strengths (Koster et al.,
2004; Qu and Hall, 2014) and inherent land surface related
predictability (Douville et al., 2007; Dirmeyer et al.,
2013) are still subjects of considerable research effort.
These biases and uncertainties are problematic, because they affect, among
others, forecast skill (Koster et al., 2010a),
regional climate change patterns (Campoy et al., 2013; Seneviratne et al., 2013; Koven et
al., 2012) and explicable trends in water resources (Lehning, 2013).
In addition, there is evidence of the presence of large-scale systematic
biases in some aspects of land hydrology in current climate models
(Mueller and Seneviratne, 2014) and the terrestrial component
of the carbon cycle (Anav et al., 2013;
Mystakidis et al., 2016). Notably, land surface processes can be an important
reason for a direct link between the climate models' temperature biases in
the present period and in the future projections with increased radiative
forcings at the regional scale (Cattiaux et al., 2013).
For snow cover, a better understanding of the links with climate is critical
for interpretation of the observed dramatic reduction in springtime snow
cover over recent decades (e.g., Derksen and Brown,
2012; Brutel-Vuilmet et al., 2013), to improve the seasonal to interannual
forecast skill of temperature, runoff and soil moisture (e.g.,
Thomas et al., 2016; Peings et al., 2011) and
to adequately represent polar warming amplification in the Arctic (e.g.,
Holland and Bitz, 2003). Snow-related biases in climate models
may arise from the snow-albedo feedback (Qu and Hall,
2014; Thackeray et al., 2015), but also from the energy sink induced by snow
melting in spring and the thermal insulation effect of snow on the
underlying soil (Koven et al., 2012;
Gouttevin et al., 2012). Temporal dynamics of snow–atmospheric coupling
during various phases of snow depletion (Xu and
Dirmeyer, 2011, 2012) are crucial for a proper representation of the timing
and atmospheric response to snow melt. Phase 1 and 2 of the Snow Model
Intercomparison Project (SnowMIP) (Etchevers
et al., 2004; Essery et al., 2009) provided useful insights in
the capacity of snow models of different complexity to simulate the snowpack
evolution from local meteorological forcing but did not explore snow–climate
interactions. Because of strong snow/atmosphere interactions, it remains
difficult to distinguish and quantify the various potential causes for
disagreement between observed and modeled snow trends and the related
climate feedbacks.
Soil moisture plays a central role in the coupled land–vegetation–snow–water–atmosphere system (Seneviratne et al.,
2010; van den Hurk et al., 2011), where interactions are
evident at many relevant timescales: diurnal cycles of land surface fluxes,
seasonal and subseasonal predictability of droughts, floods and hot
extremes, annual cycles governing the water buffer in dry seasons and
shifts in the climatology in response to changing patterns of precipitation
and evaporation. The representation of historical variations in land water
availability and droughts still suffer from large uncertainties, due to
model parameterizations, unrepresented hydrologic processes such as lateral
groundwater flow, lateral flows connected to re-infiltration of river water
or irrigation with river water, and/or atmospheric forcings (Sheffield et al., 2012; Zampieri et al., 2012; Trenberth et al., 2014;
Greve et al., 2014; Clark et al., 2015). This also
applies to the energy and carbon exchanges between the land and the
atmosphere (e.g., Mueller and Seneviratne, 2014; Friedlingstein et al., 2013).
It is difficult to generate reliable observations of soil moisture and land
surface fluxes that can be used as boundary conditions for modeling and
predictability studies. Satellite retrievals, in situ observations, offline
model experiments (Second Global Soil Wetness Project, GSWP2;
Dirmeyer et al., 2006) and indirect estimates all have a
potential to generate relevant information but are largely inconsistent,
covering different model components, and suffer from methodological flaws
(Mueller et al., 2013;
Mao et al., 2015). As a consequence, the pioneering work on deriving
soil moisture related land–atmosphere coupling strength
(Koster et al., 2004) and regional/global climate
responses in both present and future climate (Seneviratne et al., 2006, 2013) has been carried out
using (ensembles of) modeling experiments. The second Global Land
Atmosphere Coupling Experiment (GLACE2; Koster et al.,
2010a) measured the actual temperature and precipitation skill improvement
of using GSWP2 soil moisture initializations, which is much lower than
suggested by the coupling strength diagnostics. Limited quality of the
initial states, limited predictability and poor representation of essential
processes determining the propagation of information through the
hydrological cycle in the models all play a role.
Altogether, there are substantial challenges concerning both the
representation of land surface processes in current-generation ESMs and the
understanding of related climate feedbacks. The Land Surface, Snow and Soil
moisture Model Intercomparison Project (LS3MIP) is designed to allow the
climate modeling community to make substantial progress in addressing these
challenges. It is part of the sixth phase of the Coupled Model
Intercomparison Project (CMIP6; Eyring et al., 2016). The following section
further develops the objectives and rationale of LS3MIP. The experimental
design and analysis plan is presented thereafter. The final discussion
section describes the expected outcome and impact of LS3MIP.
Objectives and rationale
The goal of the collection of LS3MIP experiments is to provide a
comprehensive assessment of land surface, snow and soil moisture–climate
feedbacks, and to diagnose systematic biases and process-level deficiencies
in the land modules of current ESMs. While vegetation, carbon cycle, soil
moisture, snow, surface energy balance and land–atmosphere interaction are
all intimately coupled in the real world, LS3MIP focuses – necessarily –
on the physical land surface in this complex system: interactions with
vegetation and carbon cycle are included in the analyses wherever possible
without losing this essential focus. In the complementary experiment Land
Use MIP (LUMIP; see Lawrence et al., 2016)
and C4MIP (Jones et al., 2016)
vegetation, the terrestrial carbon cycle and land management are the central
topics of analysis. LS3MIP and LUMIP share some model experiments and
analyses (see below) to allow to be addressed the complex interactions at
the land surface and yet remain able to focus on well-posed hypotheses and
research approaches.
LS3MIP will provide the means to quantify the associated uncertainties and
better constrain climate change projections, of particular interest for
highly vulnerable regions (including densely populated regions, the Arctic,
agricultural areas, and some terrestrial ecosystems).
The LS3MIP experiments collectively address the following objectives:
evaluate the current state of land processes including surface
fluxes, snow cover and soil moisture representation in CMIP DECK (Diagnostic,
Evaluation and Characterization of Klima) experiments and CMIP6 historical
simulations (Eyring et al., 2016), to identify the main systematic biases and
their dependencies;
estimate multi-model long-term terrestrial energy/water/carbon
cycles, using the land modules of CMIP6 models under observation-constrained
historical (land reanalysis) and projected future (impact assessment)
climatic conditions considering land use/land cover changes;
assess the role of snow and soil moisture feedbacks in the
regional response to altered climate forcings, focusing on controls of
climate extremes, water availability and high-latitude climate in historical
and future scenario runs;
assess the contribution of land surface processes to systematic
Earth system model biases and the current and future predictability of
regional temperature/precipitation patterns.
These objectives address each of the three CMIP6 overarching questions: (1) What are regional feedbacks and responses to climate change?; (2) What are
the systematic biases in the current climate models?; and (3) What are the
perspectives concerning the generation of predictions and scenarios?
LS3MIP encompasses a family of model experiments building on earlier
multi-model experiments, particularly (a) offline land surface experiments
(GSWP2 and its successor GSWP3), (b) the coordinated snow model
intercomparisons SnowMIP phase 1 and 2 (Etchevers et al., 2004; Essery et
al., 2009), and (c) the coupled climate timescale GLACE-type configuration
(GLACE-CMIP, Seneviratne et al., 2013). Within LS3MIP the Land-only
experimental suite is referred to as LMIP (Land Model Intercomparison
Project) with the experiment ID Land, while the coupled suite is labeled as
LFMIP (Land Feedback MIP). A detailed description of the model design is
given below, and a graphical display of the various components within LS3MIP
is shown in Fig. 1.
As illustrated in Fig. 2, LS3MIP is addressing multiple WCRP Grand
Challenges and core projects. The LMIP experiment will provide better
estimates of historical changes in snow and soil moisture at global scale,
thus allowing the evaluation of changes in freshwater, agricultural drought
and streamflow extremes over continents and a better understanding of the
main drivers of these changes. The LFMIP experiments are of high relevance
for the assessment of key feedbacks and systematic biases of land surface processes in coupled mode (Dirmeyer et al., 2015), and are
particularly focusing on two of the main feedback loops over land: the
snow-albedo–temperature feedback involved in Arctic Amplification, and the
soil moisture–temperature feedback leading to major changes in temperature
extremes (Douville et al., 2016). In addition, LS3MIP will
allow the exchange of data and knowledge across the snow and soil moisture
research communities that address a common physical topic: terrestrial water
in liquid and solid form. Snow and soil moisture dynamics are often
interrelated (e.g., Hall et al., 2008; Xu and
Dirmeyer, 2012) and jointly contribute to hydrological variability (e.g.,
Koster et al., 2010b).
LS3MIP will also provide relevant insights for other research communities,
such as global reconstructions of land variables that are not directly
observed for detection and attribution studies (Douville
et al., 2013), estimates of freshwater inputs to the oceans (which are
relevant for sea-level changes and regional impacts;
Carmack et al., 2015), the assessment of feedbacks
shown to strongly modulate regional climate variability relevant for
regional climate information, as well as the investigation of land climate
feedbacks on large-scale circulation patterns and cloud occurrence
(Zampieri and Lionello, 2011). This will thus
also imply potential contributions to programmes like the Inter-Sectoral
Impact Model Intercomparison Project (ISIMIP; Warszawski et al., 2014) and the International Detection and Attribution Group IDAG. LS3MIP
is geared to extend and consolidate available data, models and theories to
support human awareness and resilience to highly variable environmental
conditions in a large ensemble of sectoral domains, including disaster risk
reduction, food security, public safety, nature conservation and societal
wellbeing.
Structure of the “LandMIPs”. LS3MIP includes (1) the offline
representation of land processes (LMIP) and (2) the representation of
land–atmosphere feedbacks related to snow and soil moisture (LFMIP). Forcing
associated with land use is assessed in LUMIP. Substantial links also exist
to C4MIP (terrestrial carbon cycle). Furthermore, a land albedo test bed
experiment is planned within GeoMIP. From Seneviratne et al. (2014).
Figure 3 illustrates the embedding of LS3MIP within CMIP6. LS3MIP fills a
major gap by considering systematic land biases and land feedbacks. In this
context, LS3MIP is part of a larger “LandMIP” series of CMIP6 experiments
fully addressing biases, uncertainties, feedbacks and forcings from the land
surface (Fig. 1), which are complementary to similar experiments for ocean
or atmospheric processes (Seneviratne et al., 2014).
In particular, we note that while LS3MIP focuses on systematic biases in
land surface processes (Land) and on feedbacks from the land surface
processes on the climate system (LFMIP), the complementary Land Use MIP
(LUMIP) experiment addresses the role of land use forcing on the climate
system. The role of vegetation and carbon stores in the climate system is a
point of convergence between LUMIP, C4MIP and LS3MIP, and the offline LMIP
experiment will serve as land-only reference experiments for both the LS3MIP
and LUMIP experiments. In addition, there will also be links to the C4MIP
experiment with respect to impacts of snow and soil moisture processes (in
particular droughts and floods) on terrestrial carbon exchanges and
resulting feedbacks to the climate system.
Relevance of LS3MIP for WCRP Core Projects and Grand
Challenges2.
Experimental design
The experimental design of LS3MIP consists of a series of offline land-only
experiments (LMIP) driven by a land surface forcing data set and a variety
of coupled model simulations (LFMIP) (see Fig. 4 and Table 1):
Embedding of LS3MIP within CMIP6. Adapted from Eyring et
al. (2015).
Offline land model experiments (“Land offline MIP”,
experiment ID “Land”)
Offline simulations of land surface states and fluxes allow for the
evaluation of trends and variability of snow, soil moisture and land surface
fluxes, carbon stocks and vegetation dynamics, and climate change impacts.
Within the CMIP6 program various Model Intercomparison Projects make use of
offline terrestrial simulations to benchmark or force coupled climate model
simulations: LUMIP focusing on the role of land use/land cover change, C4MIP
to address the terrestrial component of the carbon cycle and its feedback to
climate, and LS3MIP to provide soil moisture and snow boundary conditions.
http://wcrp-climate.org/index.php/grand-challenges;
status December 2015
Schematic diagram for the experiment structure of LS3MIP. Tier 1
experiments are indicated with a heavy black outline, and complementary
ensemble experiments are indicated with white hatched lines. Land-Altforce
represents three alternative forcings for the Land-Hist experiment. For further
details on the experiments and acronyms, see Table 1 and text.
Meteorological forcings, ancillary data (e.g., land use/cover changes,
surface parameters, CO2 concentration and nitrogen deposition) and
documented protocols to spin-up and execute the experiments are essential
ingredients for a successful offline land model experiment (Wei et al., 2014). The first Global
Soil Wetness Project (GSWP; Dirmeyer et al., 1999), covering two annual cycles (1987–1988),
established a successful template, which was updated and fine-tuned in a
number of follow-up experiments, both with global (Dirmeyer
et al., 2006; Sheffield et al., 2006) and regional (Boone et al., 2009) coverage.
Summary of LS3MIP experiments. Experiments with specific treatment
of subsets of land surface features are not listed in this overview.
Experiment ID and tierExperiment description/ designConfig (L/A/O)aStartEndNo. ensbNo. total yearscScience question and/or gap being addressedSynergies with other CMIP6 MIPsLand-Hist (1)land only simulationsL185020141165historical land simulationsLUMIP, C4MIP, CMIP6 historicalLand-Hist-cruNcep Land-Hist-princeton Land-Hist-wfdei (2)land only simulationsL190120143342as Land-Hist but with three different forcing data sets (Princeton forcing, CRU-NCEP, and WFDEI)Land-Future (2)land only simulationsL201521006516climate trend analysisLUMIP, C4MIP, ScenarioMIPLFMIP-pdLC (1)prescribed land conditions 1980– 2014 climateLAO198021001121diagnose land-climate feedback including ocean responseScenarioMIPLFMIP-pdLC2 (2)as LFMIP-pdLC with multiple model membersLAO198021004484diagnose land-climate feedback including ocean responseScenarioMIPLFMIP-pdLC + SST (2)prescribed land conditions 1980– 2014 climate; SSTs prescribedLA198021005605diagnose land-climate feedback over landScenarioMIPLFMIP-Pobs + SST (2)land conditions from Land-Hist; SSTs prescribedLA190120141115“perfect boundary condition” simula- tionsLFMIP-rmLC (1)prescribed land conditions 30-year running meanLAO198021001121diagnose land-climate feedback including ocean responseScenarioMIPLFMIP-rmLC2 (2)as LFMIP-rmLC with multiple model membersLAO198021004484diagnose land-climate feedback including ocean responseScenarioMIPLFMIP-rmLC + SST (2)prescribed land conditions 30-year running mean; SSTs prescribedLA198021005605diagnose land-climate feedback over landScenarioMIPLFMIP-Pobs (2)ptbdinitialized pseudo-observations landLAO1980201410350land-related seasonal predictabilityCMIP6 historical
a Config L/A/O refers to
land/atmosphere/ocean model configurations. b No. ens refers to
number of ensemble members. c No. total years is total number of
simulation years. ptbd experimental protocol needs to be detailed
in a later stage.
Available data sets for meteorological forcing
Offline experiments will primarily use
GSWP33
http://hydro.iis.u-tokyo.ac.jp/GSWP3/
(Tier 1) forcing (Kim et al., 2016) with alternate forcing used in Tier 2
experiments.
The third Global Soil Wetness Project (GSWP3) provides meteorological
forcings for the entire 20th century and beyond, making extensive use
of the 20th Century Reanalysis (20CR) (Compo et al., 2011). In this reanalysis product only surface pressure and monthly
sea-surface temperature and sea-ice concentration are assimilated. The
ensemble uncertainty in the synoptic variability of 20CR varies with the
time-changing observation network. High correlations for geopotential height
(500 hPa) and air temperature (850 hPa) with an independent long record
(1905–2006) of upper-air data were found (Compo et al., 2011), comparable to forecast skill of a state-of-the-art forecasting system
at 3 days lead time.
GSWP3 forcing data are generated based on a dynamical downscaling of 20CR. A
simulation of the Global Spectral Model (GSM), run at a T248 resolution
(∼ 50 km) is nudged to the vertical structures of 20CR zonal
and meridional winds and air temperature using a spectral nudging dynamical
downscaling technique that effectively retains synoptic features in the
higher spatial resolution (Yoshimura and
Kanamitsu, 2008). Additional bias corrections using observations, vertical
damping (Hong and Chang, 2012) and single ensemble member correction
(Yoshimura and Kanamitsu, 2013) are applied,
giving considerable improvements.
Weedon et al. (2011) provide the meteorological
forcing data for the EU Water and Global Change (WATCH) programme4
http://www.eu-watch.org/
, designed to evaluate global hydrological trends
and impacts using offline modeling. The half-degree resolution, 3-hourly
WATCH Forcing Data (WFD) was based on the ECMWF ERA-40 reanalysis and
included elevation correction and monthly bias correction using CRU
observations (and alternative GPCC precipitation total observations). WATCH
hydrological modeling led to the WaterMIP study (Haddeland
et al., 2011). The WFD stops in 2001, but within a follow-up project EMBRACE
Weedon et al. (2014) generated the WFDEI data set that starts
in 1979 and was recently extended to 2014. The WFDEI was based on the WATCH
Forcing Data methodology but used the ERA-Interim reanalysis (4D-var and
higher spatial resolution than ERA-40) so that there are offsets for some
variable in the overlap period with the WFD. The forcing consists of
3-hourly ECMWF ERA-Interim reanalysis data (WFD used ERA-40) interpolated to
half degree spatial resolution. The 2 m temperatures are bias-corrected in
terms of monthly means and monthly average diurnal temperature range using
CRU half degree observations. The 2 m temperature, surface pressure, specific
humidity and downwards longwave radiation fluxes are sequentially elevation
corrected. Shortwave radiation fluxes are corrected using CRU cloud cover
observations and corrected for the effects of seasonal and interannual
changes in aerosol loading. Rainfall and snowfall rates are corrected using
CRU wet days per month and according to CRU or GPCC observed monthly
precipitation gauge totals. The WFDEI data set is also used as forcing to
the ISIMIP2.1 project, which focuses on historical validation of global
water balance under transient land use change (Warszawski et al., 2014).
(Le Quere
et al., 2009) with annual updates of global carbon pools and fluxes, the
offline modeling framework TRENDY6
http://dgvm.ceh.ac.uk/node/21
applies an ensemble of terrestrial carbon allocation and land surface
models. For this a forcing data set is prepared in which NCEP reanalysis
data are bias corrected using the gridded in situ climate data from the
Climate Research Unit (CRU), the so-called CRU-NCEP data set (Viovy and Ciais, 2009).
This data set is currently available from 1901 to 2014 at 0.5∘
horizontal spatial resolution and 6-hourly time step. It is being updated
annually.
The Princeton Global Forcing data
set7
http://hydrology.princeton.edu/data.php
(Sheffield et al., 2006) was developed as a forcing for land surface and
other terrestrial models, and for analyzing changes in near-surface climate.
The data set is based on 6-hourly surface climate from the NCEP-NCAR
reanalysis, which is corrected for biases at diurnal, daily and monthly
timescales using a variety of observational data sets. The data are available
at 1.0, 0.5 and 0.25∘ resolution and 3-hourly time step. The latest
version (V2.2) covers 1901–2014, with a real-time extension based on
satellite precipitation and weather model analysis fields. The reanalysis
precipitation is corrected by adjusting the number of rain days and monthly
accumulations to match observations from CRU and the Global Precipitation
Climatology Project (GPCP). Precipitation is downscaled in space using
statistical relationships based on GPCP and the TRMM Multi-satellite
Precipitation Analysis (TMPA), and to 3-hourly resolution based on TMPA.
Temperature, humidity, pressure and longwave radiation are downscaled in
space with account for elevation. Daily mean temperature and diurnal
temperature range are adjusted to match the CRU monthly data. Shortwave and
longwave surface radiation are adjusted to match satellite-based observations
from the University of Maryland (Zhang et al., 2016) and to be consistent
with CRU cloud cover observations outside of the satellite period. An
experimental version (V3) assimilates station observations into the
background gridded field to provide local-scale corrections (J. Sheffield,
personal communication, February 2016).
Taylor diagram for evaluating the forcing data sets comparing to
daily observations from FLUXNET sites, as used by (Best et al., 2015):
(a) 2 m air temperature and (b) precipitation. Red, blue
and green dots indicate GSWP3, Watch Forcing Data (Weedon et al., 2011) and
Princeton forcing (Sheffield et al., 2006), respectively. Grey and orange
dots indicate 20CR and its dynamically downscaled product (GSM248).
Figure 5 shows the performance in terms of correlation and standard
deviation of the forcing data sets compared to daily observations from 20 globally
distributed in situ FLUXNET sites (Baldocchi et al., 2001).
Although for precipitation intrinsic heterogeneity leads to significant
differences with the in situ observations, longwave and shortwave downward
radiation (not shown) and air temperature show variability characteristics
similar to the observations.
The participating modeling groups are invited to run a number of
experiments in this land-only branch of LS3MIP.
Historical offline simulations: Land-Hist
The Tier 1 experiments of the offline LMIP experiment consist of simulations
using the GSWP3 forcing data for a historical (1831–2014) interval. The land
model configuration should be identical to that used in the DECK and CMIP6
historical simulations for the parent coupled model.
The atmospheric forcing will be prepared at a standard 0.5×0.5∘ spatial resolution at 3-hourly intervals and distributed with
a package to regrid data to the native grids of the global climate models (GCMs). Also vegetation,
soil, topography and land/sea mask data will be prescribed following the
protocol used for the CMIP6 DECK simulations. Spin-up of the land-only
simulations should follow the TRENDY protocol8
http://dgvm.ceh.ac.uk/node/9
which calls for recycling of the climate mean
and variability from two decades of the forcing data set (e.g., 1831–1850 for
GSWP3, 1901–1920 for the alternative land surface forcings). Land use should
be held constant at 1850 as in the DECK 1850 coupled control simulation
(piControl). See discussion and definition of “constant land-use” in Sect. 2.1 of
LUMIP protocol paper (Lawrence et al., 2016). CO2 and all other forcings should be held constant at 1850
levels during spinup. For the period 1850 to the first year of the forcing
data set, the forcing data should continue to be recycled but all other
forcings (land-use, CO2, etc.) should be as in the CMIP6 historical
simulation. Transient land use is a prescribed CMIP6 forcing and is
described in the LUMIP protocol (Lawrence et al., 2016).
Interactions with the ocean MIP (OMIP; Griffies
et al., 2016) are arranged by the use of terrestrial freshwater fluxes
produced in the LMIP simulations as a boundary condition for the forced
ocean-only simulations in OMIP, in addition to the forcing provided by
(Dai and Trenberth 2002).
Single site time series of in situ observational forcing variables from
selected reference locations (from FLUXNET, Baldocchi et al., 2001) are
supplied in addition to the forcing data for additional site level
validation. This allows the evaluation of land surface models in current
GCMs such as applied by Best et al. (2015)
and in ESM-SnowMIP (Earth System Model – Snow Module Intercomparison
Project; see below). For snow evaluation, an international network of
well-instrumented sites has been identified, covering the major climate
classes of seasonal snow, each of which poses unique challenges for the
parameterization of snow related processes (see analysis strategy below).
Although Land-Hist is not a formal component of the DECK simulations which
form the core of CMIP6 (see Fig. 3), the WCRP Working Group on Climate
Modeling (WGCM) recognized the importance of these land-only experiments
for the process of model development and benchmarking. A future
implementation of a full or subset of this historical run is proposed to
become part of the DECK in future CMIP exercises and is included as a Tier 1
experiment in LS3MIP. Land surface model output from this subset of LMIP
will also be used as boundary condition in some of the coupled climate model
simulations, described below.
Global distributions of the similarity index (Ω) for
2001–2010 of monthly mean (a, c) and (b, d) monthly
variance (calculated from daily data from each data set) of 2 m air
temperature (top panels) and precipitation (bottom panels), respectively.
Shown are global distributions and zonal means. After Kim (2010).
Historical simulations with alternative forcings
Additional Tier 2 experiments are solicited where the experimental setup is
similar to the Tier 1 simulations, but using 3 alternative meteorological
forcing data sets that differ from GSWP3: the Princeton forcing
(Sheffield et al., 2006), WFD and
WFDEI combined (allowing for offsets as needed; Weedon et al., 2014) and the CRU-NCEP forcing (Wei et al., 2014) used in TRENDY
(Sitch et al., 2015). These
Tier 2 experiments cover the period 1901–2014. The model outputs will
allow assessment of the sensitivity of land-only simulations to
uncertainties in forcing data. Differences in the outputs compared to the
primary runs with the GSWP3 forcing will help in understanding simulation
sensitivity to the selection of forcing data sets. Kim (2010)
utilized a similarity index (Ω; Koster
et al., 2000) to estimate the uncertainty derived from an ensemble of
precipitation observation data sets relative to the uncertainty from an
ensemble of model simulations for evapotranspiration and runoff. The joint
utilization of common monthly observations by the various forcing data sets
leads to a high value of Ω when evaluated using monthly mean values.
However, evaluation of data set consistency of monthly variance leads to much
larger disparities and considerably lower values of Ω (Fig. 6).
This uncertainty will propagate differently to other hydrological variables,
such as runoff or evapotranspiration (Kim, 2010).
Climate change impact assessment: Land-Future
A set of future land-only time slice simulations (2015–2100) will be
generated via forcing data obtained from at least 2 future climate scenarios
from the ScenarioMIP (O'Neill et al., 2016) and will be executed at a later stage during CMIP6. Tentatively,
Shared Socioeconomic Pathway SSP5-8.5 and SSP4-3.79
will be selected,
run by 3 model realizations each. The models will be chosen based on the
evaluation of the results from the Historical simulations from the CMIP6
Nucleus in order to represent the ensemble spread efficiently and reliably
(Evans et al., 2013). To generate a set of ensemble forcing
data for the future, a trend preserving statistical bias correction method
will be applied to the 3-hourly surface meteorology variables (Table A4)
from the scenario output (Hempel et al., 2013;
Watanabe et al., 2014). Gridded forcings will be provided in a similar data
format as the historical simulations.
Land-Future is a Tier 2 experiment in LS3MIP and focuses on assessment of
climate change impact (e.g., shifts of the occurrence of critical water
availability due to changing statistical distributions of extreme events)
and on the assessment of the land surface analogue of climate sensitivity
for various key land variables (Perket et al., 2014; Flanner et al., 2011).
Prescribed land surface states in coupled models for land
surface feedback assessment (“Land Feedback MIP”, LFMIP)
Land surface processes do not act in isolation in the climate system. A
tight coupling with the overlying atmosphere takes place on multiple
temporal and spatial scales. A systematic assessment of the strength and
spatial structure of land surface interaction at subcontinental, seasonal
timescales has been performed with the initial GLACE setup (GLACE1 and
GLACE2 experiments; Koster et al., 2004) in which
essentially the spread in an ensemble simulation of a coupled
land–atmosphere model was compared to a model configuration in which the
land–atmosphere interaction was greatly bypassed by prescribing soil
conditions throughout the simulation in all members of the ensemble.
Examination of the significance of land–atmosphere feedbacks at the
centennial climate timescale was later explored at the regional scale in a
single-model study (Seneviratne et al., 2006) and on global scale
in the GLACE-CMIP5 experiment in a small model ensemble (Seneviratne et al., 2013).
A protocol very similar to the design of GLACE-CMIP5 is followed in LFMIP.
Parallel to a set of reference simulations taken from the CMIP6 DECK, a set
of forced experiments is carried out where land surface states are
prescribed from or nudged towards predescribed fields derived from coupled
simulations. The land surface states are prescribed or nudged at a daily
timescale. This setup is similar to the Flux Anomaly Forced MIP (FAFMIP,
Gregory et al., 2016), where the role of
ocean–atmosphere interaction at climate timescales is diagnosed by
idealized surface perturbation experiments.
While earlier experiments used model configurations with prescribed SST and
sea ice conditions, the Tier 1 experiment in LFMIP will be based on coupled
atmosphere–ocean global climate model (AOGCM) simulations and comprise simulations for a historical (1980–2014) and
future (2015–2100) time range. The selection of the future scenario (from
the ScenarioMIP experiment) will be based on the choices made in the offline
LMIP experiment (see above).
In GLACE-CMIP5 only soil moisture states were prescribed in the forced
experiments. The configuration of the particular land surface models may
introduce the need to make different selections of land surface states to be
prescribed, for instance to avoid strong inconsistencies in the case of
frozen ground (soil moisture rather than soil water state should be
prescribed; M. Hauser, ETH Zurich, personal communication, 2015), melting snow or growing vegetation. Prescribing surface soil
moisture only (experiment “S” in Koster
et al., 2006) gave unrealistic values of the surface Bowen ratio. A
standardization of this selection is difficult as the implementation and
consequences may be highly model specific. Here we recommend to prescribe
only the water reservoirs (soil moisture, snow mass). The disparity of
possible implementations is adding to the uncertainty range generated by the
model ensemble, similar to the degree to which implementation of land use,
flux corrections or downscaling adds to this uncertainty range.
Participating modeling groups are encouraged to apply various test
simulations focusing both on technical feasibility and experimental impact
to evaluate different procedures to prescribe land surface conditions.
The earlier experience with GLACE-type experiments has revealed a number of
technical and scientific issues. Because in most GCMs the land surface
module is an integral part of the code describing the atmosphere,
prescribing land surface dynamics requires a non-conventional technical
interface, reading and replacing variables throughout the entire
simulations. Many LS3MIP participants have participated earlier in
GLACE-type experiments, but for some the code adjustments will require a
technical effort. Interpretation of the effect of the variety of
implementations of prescribed land surface variables by the different
modeling groups (see above) is helped by a careful documentation of the way
the modeling groups have implemented this interface. Tight coordination and
frequent exchange among the participating modeling groups on the technical
modalities of the implementation of the required forcing methods will be
ensured during the preparatory phase of LS3MIP in order to maximize the
coherence of the modeling exercise and to facilitate the interpretation of
the results.
By design, the prescribed land surface experiments do not fully conserve
water and energy, similar to the setup of the Atmospheric Model Intercomparison
Project (AMIP), nudged and data assimilation
experiments. A systematic addition or removal of water or energy can even
emerge as a result of asymmetric land surface responses to dry and to wet
conditions, e.g., when surface evaporation or runoff depend strongly
non-linearly to soil moisture or snow states (e.g., Jaeger and
Seneviratne, 2011). Also, unrepresented processes (such as water extraction
for irrigation or exchange with the groundwater) may lead to imbalances in
the budget (Wada et al., 2012). This systematic alteration of the
water and energy balance may not only perturb the simulation of present-day
climate (e.g., Douville, 2003;
Douville et al., 2016) but may also interact with the projected climate
change signal, where altered climatological soil conditions can contribute
to the climate change induced temperature or precipitation signal or water
imbalances can lead to imposed runoff changes that could affect ocean
circulation and SSTs. Earlier GLACE-type experiments revealed that the
problems of water conversion are often reduced when prescribed soil water
conditions are taken as the median rather than the mean of a sample over
which a climatological mean is calculated (Hauser et al., 2016). In the analyses of the experiments this asymmetry and lack of
energy/water balance closure will be examined and put in context of the
climatological energy and water balance and its climatic trends.
To be able to best quantify the forcing that prescribing the land surface
state represents, the increments of both snow and soil moisture imposed as a
consequence of this prescription are required as an additional output. This
will enable us to estimate the amplitude of implicit water and energy fluxes
imposed by the forcing procedure.
Complementary experiments following an almost identical setup as LFMIP, but
limiting the prescription of land surface variables to snow-related
variables and thus leaving soil moisture free-running, are carried out in
the framework of the ESM-SnowMIP carried out within the WCRP Grand Challenge
“Melting Ice and Global Consequences”10
.
ESM-SnowMIP being tightly linked to LS3MIP, these complementary experiments
will allow separating effects of soil moisture and snow feedbacks.
Tier 1 experiments in LFMIP
Similar to the setup of GLACE-CMIP5 (Seneviratne et al., 2013), the core
experiments of LFMIP (tier 1) evaluate two different sets of prescribed land
surface conditions (snow and soil moisture):
LFMIP-pdLC: the experiments comprise transient coupled atmosphere–ocean
simulations in which a selection of land surface characteristics is
prescribed rather than interactively calculated in the model. This
“climatological” land surface forcing is calculated as the mean annual
cycle in the period 1980–2014 from the historical GCM simulations. The
experiment aims at diagnosing the role of land–atmosphere feedback at the
climate timescale. Seneviratne et al. (2013) found a
substantial effect of changes in climatological soil moisture on projected
temperature change in a future climate, both for seasonal mean and daytime
extreme temperature in summer. Effects on precipitation are less clear, and
the multi-model nature of LS3MIP is designed to sharpen these quantitative
effects. Also, LS3MIP will take a potential damping (or amplifying) effect
of oceanic responses on altered land surface conditions into account, in
contrast to GLACE-CMIP5. Experiments using this setup (i.e., coupled ocean)
in a single-model study have shown that the results could be slightly
affected by the inclusion of an interactive ocean, although the effects were
not found to be large overall (Orth and Seneviratne, 2016).
LFMIP-rmLC: a prescribed climatology using a transient 30-yr running mean,
where a comparison to the standard CMIP6 runs allows diagnosis of shifts in
the regions of strong land–atmosphere coupling as recorded by e.g.,
Seneviratne et al. (2006), and shifts in potential
predictability related to land surface states (Dirmeyer et al., 2013).
Both sets of simulations cover the historical period (1850–2014) and extend
to 2100, based on a forcing scenario to be identified at a later stage. The
procedure to initialize the land surface states in the ensemble members is
left to the participant, but should allow to generate sufficient spread that
can be considered representative for the climate system under
study Koster et al. (2006) proposed a
preference hierarchy of methods depending on the availability of
initialization fields, and LS3MIP will follow this proposal.
Output in high temporal resolution (daily, as well as sub-daily for some
fields and time slices) is required to address the role of land
surface–climate feedbacks on climate extremes over land.
Multi-member experiments are encouraged, but the mandatory tier 1
simulations are limited to one realization for each of the two prescribed
land surface time series described above.
Tier 2 experiments in LFMIP
To analyze a number of additional features of land–atmosphere feedbacks, a
collection of tier 2 simulations is proposed in LS3MIP.
Simulations with observed SST – The AOGCM simulations from Tier 1 are duplicated with a prescribed SST
configuration taken from the AMIP runs in the DECK atmospheric global climate model (AGCM), in order to
isolate the role of the ocean in propagating and damping/reinforcing land
surface responses on climate (Koster et al., 2000). Both the historic and running mean land surface simulations are
requested (LFMIP-pdLC + SST and -rmLC + SST, respectively).
Simulations with observed SST and Land-Hist output – A “pseudo-observed boundary condition” set of experiments use the AMIP
SSTs and the Land-Hist land boundary conditions generated by the land
surface model used in the participating ESM, leading to simulations driven
by surface fields that are strongly controlled by observed forcings. This
will only cover the historic period (1901–2014) (LFMIP-PObs + SST). For this
the land-only simulations in LMIP need to be interpolated to the native GCM
grid, preserving land–sea boundaries and other characteristics.
Separate effects of soil moisture and snow, and role of additional land parameters and variables – Additional experiments, in which only snow, snow albedo or soil moisture is
prescribed will be conducted to assess the respective feedbacks in
isolation, and have control on possible interactions between snow cover and
soil moisture content. Also vegetation parameters and variables (e.g., leaf
area index, canopy height and thickness) are considered. These experiments
are not listed in Table 1, but will be detailed in a follow-up protocol to
be defined later.
Fixed land use conditions – In conjunction with the Land Use MIP (LUMIP), a repetition of the Tier 1
experiment under fixed 1850 land cover and land use conditions highlights
the role of soil moisture in modulating the climate response to land cover
and land use (not listed in Table 1).
Prescribed land surface states derived from
pseudo-observations (LFMIP-Pobs)
The use of LMIP (land-only simulations) to initialize the AOGCM experiments
(LFMIP) allows a set of predictability experiments in line with the GLACE2
setup (Koster et al., 2010a). The LFMIP-Pobs experiment is an extension to GLACE2 by (a) allowing
more models to participate, (b) improving the statistics by extending the
original 1986–1995 record to 1980–2014, (c) evaluating the quality of
newly available land surface forcings and (d) executing the experiments in
AOGCM mode. Koster et al. (2010a) and van den Hurk et al. (2012) concluded that the
forecast skill improvement from models using initial soil moisture
conditions was relatively low. Possible causes for this low skill are the
limited record length and limited quality of the (precipitation)
observations used to generate the soil conditions. These issues are
explicitly addressed in LFMIP-Pobs.
All LFMIP-Pobs experiments are Tier 2, which also gives room for additional
model design elements such as the evaluation of various observational data
sources (such as for snow mass (Snow Water Equivalent; SWE) or snow albedo, using satellites derived, reanalysis and land
surface model outputs). The predictability assessments include the evaluation
of the contribution of snow cover melting and its related feedbacks to the
underestimation of recent boreal polar warming by climate models.
The experimental protocol (number of simulations years, ensemble size,
initialization, model configuration, output diagnostics) has a strong impact
on the results of the experiment (e.g., Guo and Dirmeyer, 2013).
This careful design of the LFMIP-Pobs experiment needed for a successful
implementation has currently not yet taken place. Therefore these
experiments are listed as Tier 2 in Table 1, with the comment that the
detailed experimental protocol still needs to be defined.
Analysis strategy
LS3MIP is designed to push the land surface component of climate models,
observational data sets and projections to a higher level of maturity.
Understanding the propagation of model and forecast errors and the design of
model parameterizations is essential to realize this goal. The LS3MIP
steering group is a multi-disciplinary team (climate modelers, snow and
soil moisture model specialists, experts in local and remotely sensed data
of soil moisture and snow properties) that ensures that the experiment
setups, model evaluations and analyses/interpretations of the results are
pertinent.
For both snow and soil moisture the starting point will be a careful
analysis of model results from on the one hand (a) the DECK historic
simulations (both the AMIP and the historical coupled simulation) and (b) on
the other hand the (offline) LMIP historical simulations.
For the evaluation of snow representation in the models, large-scale
high-quality data sets of snow mass (SWE) and snow cover extent (SCE) with
quantitative uncertainty characteristics will be provided by the Satellite
Snow Product Intercomparison and Evaluation Experiment (SnowPEX11
http://calvalportal.ceos.org/projects/snowpex
). Analysis within SnowPEX is
providing the first evaluation of satellite derived snow extent (15 participating data sets) and SWE derived from satellite measurements, land
surface assimilation systems, physical snow models and reanalyses (7 participating data sets). Internal consistency between products, and bias
relative to independent reference data sets are being derived based on
standardized and consistent protocols. The evaluation of variability and
trends in terrestrial snow cover extent and mass was examined previously for
CMIP3 and CMIP5 by e.g., Brown and Mote (2009), Derksen and Brown (2012) and Brutel-Vuilmet et al. (2013). While these assessments were based on single observational
data sets, and hence provide no perspective on observational uncertainty and
spread relative to multi-model ensembles, standardized multi-source data sets
generated by SnowPEX will allow assessment using a multi-data-set
observational ensemble (e.g., Mudryk et al., 2015). For
snow albedo, multiple satellite-derived data sets are available, including
16-day MODIS12
http://modis-atmos.gsfc.nasa.gov/ALBEDO/
data from
2001–present, the ESA GlobAlbedo product13
product (1982–2011), and a derivation of the snow
shortwave radiative effect from 2001–2013 (Singh et al., 2015). Satellite retrievals of snow cover
fraction in forested and mountainous areas is an ongoing area of uncertainty
which influences the essential diagnostics related to climate sensitivity of
snow cover (Thackeray et al., 2015), feeding into essential
diagnostics related to climate sensitivity of snow cover (Qu and
Hall, 2014; Fletcher et al., 2012).
In the case of soil moisture, land hydrology and vegetation state, several
observations-based data sets will be used in the evaluation of the coupled
DECK simulations and offline Land experiments. Data considered will include
the first multidecadal satellite-based global soil moisture record
(Essential Climate Variable Soil Moisture ECVSM)
(Liu et al., 2012; Dorigo et al., 2012), long-term (2002–2015) records of terrestrial water storage from the
GRACE satellite (Rodell
et al., 2009; Reager et al., 2016; Kim et al., 2009), the multi-product
LandFlux-EVAL evapotranspiration synthesis (Mueller et al., 2013),
multi-decadal satellite retrievals of the Fraction of Photosynthetically
Absorbed Radiation (FPAR, e.g., Gobron et al., 2010;
Zscheischler et al., 2015), and upscaled Fluxnet based products (Jung et al., 2010).
Several details of snow and soil moisture dynamical processes can be
indirectly inferred through the analysis of river discharge
(Orth et al., 2013; Zampieri et al., 2015). Variables
simulated by the routing schemes included in the land surface models can be
compared with the station data available from the Global Runoff Database
(GRDC15
http://www.bafg.de/GRDC
). Combined use of in situ discharge
observations and terrestrial water storage changes observed by GRACE will
verify how the land surface simulations partition the terms in the water
balance equation (i.e., precipitation, evapotranspiration, runoff and water
storage changes)(Kim et al., 2009).
The coupled LS3MIP (LFMIP) simulations will be analyzed in concert with the
control runs to quantify various climatic effects of snow and soil moisture,
detect systematic biases and diagnose feedbacks. Anticipated analyses
include the following.
Drivers of variability at multiple timescales – Comparison of simulations with prescribed soil moisture and snow
(LFMIP-pdLC) allows quantification of the impact of land surface state
variability on variability of climate variables such as temperature,
relative humidity, cloudiness, precipitation and river discharge at several
timescales. The LFMIP-rmLC simulation allows evaluation of this
contribution on seasonal timescales, and changes of patterns of high/low
land surface impact in a future climate. In particular, a focus will be put
on impacts on climate extremes (temperature extremes, heavy precipitation
events, see e.g., Seneviratne et al., 2013) and the
possible role of land-based feedbacks in amplifying regional climate
responses compared to changes in global mean temperature
(Seneviratne et al., 2016). A secondary focus will be on
the impacts of snow and soil moisture variability on the extremes of river
discharge, which can be related to large-scale floods and to non-local
propagation of drought signals. These aspects will be analyzed in the
context of water management and to quantify feedbacks of river discharge to
the climate system (through the discharge in the oceans, Materia et al., 2012;
Carmack et al., 2015) and to the carbon cycle (through the methane produced
in flooded areas, Meng et al., 2015).
Attribution of model disagreement – The multi-model set up of the experiment allows closer inspection of the
effects of modeled soil moisture and snow (and related processes such as
plant transpiration, photosynthesis, or snowmelt) on calculated land
temperature, precipitation, runoff, vegetation state, and gross primary
production. The comparison of LFMIP-pdLC and LFMIP-rmLC will be useful to
isolate model disagreement in land surface feedbacks potentially induced by
including coupling to a dynamic ocean despite similar land response to
climate change.
Emergent constraints – While the annual cycle of snow cover and local temperature (Qu and
Hall, 2014), and the relation between global mean temperature fluctuations
and CO2-concentration (Cox et al., 2013) provide
observational constraints on snow-albedo and carbon–climate feedback,
respectively, similar emergent constraints may be defined to constrain
(regional) soil moisture or snow related feedbacks with temperature or
hydrological processes such as, for instance, the timing of spring onset
which may be related to snowmelt, spring river discharge
(Zampieri et al., 2015) and vegetation phenology (Xu et al., 2013). Use of
appropriate observations and diagnostics as emergent constraints will reduce
uncertainties in projections of mean climate and extremes (heat extremes,
droughts, floods) (Hoffman et al., 2014). The analysis of amplitude and timing of seasonality of hydrological
and ecosystem processes will provide additional diagnostics.
Attribution of model bias – A positive relationship between model temperature bias in the current
climate, and (regional) climate response can partly be attributed to the
soil moisture–climate feedback, which acts on both the seasonal and climate
timescale (Cheruy et al., 2014). A multi-model assessment
of this relationship is enabled via LS3MIP. The comparison of AMIP-DECK,
LFMIP-CA and LFMIP-LCA will be used to assess the impact of
atmospheric-related errors in land boundary conditions on the AGCM biases.
Changes in feedback hotspots and predictability patterns – Land surface conditions don't exert uniform influence on the atmosphere in
all areas of the globe: a distribution of strong interaction “hotspots”
and areas of high potential predictability contributions from the land
surface exists (e.g., Koster et al., 2004). These
patterns may change in a future climate (e.g., Seneviratne et al., 2006). A
multi-model assessment such as the one foreseen in LS3MIP allows mapping changes in
these patterns, with implications for the occurrence of droughts, heat
waves, irrigation limitations or river discharge anomalies and their
predictability (Dirmeyer et al., 2013).
Snow shortwave radiative effect analysis – The snow shortwave radiative effect (SSRE) can be diagnosed through
parallel calculations of surface albedo and shortwave fluxes with and
without model snow on the ground or in the vegetation canopy (Perket et al., 2014). This metric provides a precise,
overarching measure of the snow-induced perturbation to solar absorption in
each model, integrating over the variable influences of vegetation masking,
snow grain size, snow cover fraction, soot content, etc. SSRE is analogous
to the widely used cloud radiative effect diagnostic, and its time evolution
provides a measure of snow albedo feedback in the context of changing
climate (Flanner et al., 2011). We recommend that the
diagnostic snow shortwave radiative effect (SSRE) calculation be implemented
in standard LS3MIP simulations (Tiers 1 and 2). This will enable us to
evaluate the integrated effect of model snow cover on surface radiative
fluxes.
Complementary snow-related offline experiments – Additional offline experiments are enabled by the provision of a collection
of localized forcing data in the Land-Hist experiment (see above). For snow,
a network of well-equipped sites is analyzed in detail for characteristic
features (for example, snow–vegetation interactions for taiga snow; wind-driven processes for tundra snow; snow–rain partitioning for maritime snow).
Reference simulations at these sites, consistent with previous SnowMIP
experiments (Essery et al., 2009), will be complemented by additional
experiments with (1) a fixed snow albedo; and (2) the insulative properties
of snow removed in order to isolate the contributions of snow to the surface
energy budget and ground thermal regime. This will be implemented within the
ESM-SnowMIP16
initiative, aimed at improving our understanding of sources of coupled model
biases (global offline and site scale experiments) in order to identify
priority avenues for future model development.
Earth system modeling groups participating in LS3MIP.
Model nameInstituteCountryACCESSCSIRO/Bureau of MeteorologyAustraliaACME Land ModelU.S. Department of EnergyUSABCC-CSM2-MRBCC, CMAChinaCanESMCCCmaCanadaCESMUSACMCC-CM2Centro Euro-Mediterraneo sui Cambiamenti ClimaticiItalyCNRM-CMCNRM-CERFACSFranceEC-EarthSMHI and 26 other institutesSweden and 9 otherEuropean countriesFGOALSLASG, IAP, CASChinaGISSNASA GISSUSAIPSL-CM6IPSLFranceMIROC6-CGCMAORI, University of Tokyo/JAMSTEC/NationalJapanInstitute for Environmental StudiesMPI-ESMMax Planck Institute for Meteorology (MPI-M)GermanyMRI-ESM1.xMeteorological Research InstituteJapanNorESMNorwegian Climate Service CentreNorwayhadGEM3Met OfficeUK
Regarding the snow analyses, the initial geographical focus
of LS3MIP is on the continental snow cover of both hemispheres, both in
ice-free areas (Northern Eurasia and North America) and on the large ice
sheets (Greenland and Antarctica). Effects of snow on sea ice and the
quality of the representation of snow on sea ice in climate models will be
explored later, but they are of interest because of strong recent trends of Arctic
sea ice decline and the potential amplifying effect of earlier spring snow
melt over land.
For soil moisture, the geographical focus is on all land areas, with special
interest in agricultural locations with strong land–atmosphere interaction
(transition zones between wet and dry climates), extensive irrigation areas,
and high interannual variability of warm season climate in densely populated
areas.
The analyses are carried out on a standardized model output data set. A
summary of the requested output data is given in tables in the Appendix.
Time line, participating models and interaction strategy
The offline land surface experiments (Land-Hist) are expected to be
completed in early 2017. Future time slices can only be performed when the
Scenario-MIP results become available. All coupled LS3MIP simulations and
their subsequent analyses will be timed after the completion of the DECK and
historical 20th century simulations, expected by mid-2017. Table 2 lists the
participating Earth system modeling groups.
The organizational structure of LS3MIP relies on active participation of
modeling groups. Coordination structures are in place for the collection and
dissemination of data and model results (Eyring et al., 2016), and for the
organization of meetings and seminars (by the core team members of LS3MIP,
first six authors of this manuscript). Different from earlier experiments
such as GSWP2 and GLACE1/2, no central “analysis group” is put in place
that is responsible for the analyses as proposed in this manuscript. The
execution and publication of analyses is considered to be a community effort
of participating researchers, in order to avoid duplication of efforts and
coordinate the production of scientific papers.
Discussion: expected outcome and impact of LS3MIP
The treatment of the land surface in the current generation of climate
models plays a critical role in the assessment of potential effects of
widespread changes in radiative forcing, land use and biogeochemical cycles.
The land surface both “receives” climatic variations (by its atmospheric
forcing) and “returns” these variations as feedbacks or land surface
features that are of high relevance to the people living on it. The strong
coupling between land surface, atmosphere, hydrosphere and cryosphere makes
an analysis of its performance characteristics challenging: the response and
the state of the land surface strongly depend on the climatological context,
and metrics of interactions or feedbacks, which are all difficult to define
and observe (van den Hurk et al., 2011).
LS3MIP addresses these challenges by enhancing earlier diagnostic studies
and experimental designs. Within the limits to which complex models such as
ESMs can be evaluated with currently available observational evidence (see
e.g., the interesting philosophical discussion on climate model evaluation by
Lenhard and Winsberg, 2010) it will lead to enhanced understanding
of the contribution of land surface treatment to overall climate model
performance; give inspiration on how to optimize land surface
parameterizations or their forcing; support the development of better
forecasting tools, where initial conditions affect the trajectory of the
forecast and can be used to optimize forecast skill; and, last but not
least, provide a better historical picture of the evolution of our vital
water resources during the recent century. In particular, LS3MIP will
provide a solid benchmark for assessing water and climate related risks and
trends therein. Given the critical importance of changes in land water
availability and of impacts of changes in snow, soil moisture and land
surface states for the projected evolution of climate mean and extremes, we
expect that LS3MIP will help the research community make fundamental
advances in this area.
Data availability
The offline forcing data for the Land-Hist experiments and output from the
model simulations described in this paper will be distributed through the
Earth System Grid Federation (ESGF) with digital
object identifiers (DOIs) assigned. The model output required for LS3MIP is
listed in the Appendix. Model data distributed via ESGF will be freely
accessible through data portals after registration. This infrastructure makes
it possible to carry out the experiments in a distributed matter, and to
allow later participation of additional modeling groups. Links to all
forcings data sets will be made available via the CMIP Panel
website17
.
Information about accreditation, data infrastructure, metadata structure,
citation and acknowledging is provided by Eyring et al. (2016).
Output data tables requested for LS3MIP
Variable request table “LEday”: daily variables related to
the energy cycle. Priority index (p∗) in column 1 indicates 1:
“Mandatory” and 2: “Desirable”. The dimension (dim.) column indicates
T: time, Y: latitude, X: longitude, and Z: soil or snow layers.
“Direction” identifies the direction of positive numbers.
p∗Namestandard_name (cf)long_name (netCDF)UnitDirectionDim.1rsssurface_net_downward_shortwave_fluxnet shortwave radiationW m-2downwardTYX1rlssurface_net_downward_longwave_fluxnet longwave radiationW m-2downwardTYX2rsdssurface_downwelling_shortwave_flux_in_airdownward shortwave radiationW m-2downwardTYX2rldssurface_downwelling_longwave_flux_in_airdownward longwave radiationW m-2downwardTYX2rsussurface_upwelling_shortwave_flux_in_airupward shortwave radiationW m-2upwardTYX2rlussurface_upwelling_longwave_flux_in_airupward longwave radiationW m-2upwardTYX1hflssurface_upward_latent_heat_fluxlatent heat fluxW m-2upwardTYX1hfsssurface_upward_sensible_heat_fluxsensible heat fluxW m-2upwardTYX1hfdssurface_downward_heat_fluxground heat fluxW m-2downwardTYX1hfdsnsurface_downeard_heat_flux_in_snowdownward heat flux into snowW m-2downwardTYX2hfmltsurface_snow_and_ice_melt_heat_fluxenergy of fusionW m-2solid to liquidTYX2hfsblsurface_snow_and_ice_sublimation_heat_fluxenergy of sublimationW m-2solid to vaporTYX2tausurface_downward_stressmomentum fluxN m-2downwardTYX2hfrstemperature_flux_due_to_rainfall_expressed_heat transferred to snowpack by rainfallW m-2downwardTYXas_heat_flux_onto_snow_and_ice1dteschange_over_time_in_thermal_energy_change in surface heat storageJ m-2increaseTYXcontent_of_surface1dtesnchange_over_time_in_thermal_energy_change in snow/ice cold contentJ m-2increaseTYXcontent_of_surface_snow_and_ice1tssurface_temperatureaverage surface temperatureK–TYX2tsnssurface_snow_skin_temperaturesnow surface temperatureK–TYX2tcssurface_canopy_skin_temperaturevegetation canopy temperatureK–TYX2tgssurface_ground_skin_temperaturetemperature of bare soilK–TYX2trsurface_radiative_temperaturesurface radiative temperatureK–TYX1albssurface_albedosurface albedo––TYX1albsnsnow_and_ice_albedosnow albedo––TYX1sncsurface_snow_area_fractionsnow covered fraction––TYX2albccanopy_albedocanopy albedo––TYX2cncsurface_canopy_area_fractioncanopy covered fraction––TYX1tslsoil_temperatureaverage layer soil temperatureKTZYX1tsnlsnow_temperaturetemperature profile in the snowK–TZYX1tasmaxair_temperature_maximumdaily maximum near-surface airK–TYXtemperature1tasminair_temperature_minimumdaily minimum near-surface airK–TYXtemperature2cltcloud_area_fractiontotal cloud fraction––TYX
Variable request table “LWday”: daily variables related to the
water cycle.
p∗Namestandard_name (cf)long_name (netCDF)UnitDirectionDim.1prprecipitation_fluxprecipitation ratekg m-2 s-1downwardTYX2prrarainfall_fluxrainfall ratekg m-2 s-1downwardTYX2prsnsnowfall_fluxsnowfall ratekg m-2 s-1downwardTYX2prrcconvective_rainfall_fluxconvective rainfall ratekg m-2 s-1downwardTYX2prsncconvective_snowfall_fluxconvective snowfall ratekg m-2 s-1downwardTYX1prvegprecipitation_flux_onto_canopyprecipitation onto canopykg m-2 s-1downwardTYX1etsurface_evapotranspirationtotal evapotranspirationkg m-2 s-1upwardTYX1ecliquid_water_evaporation_flux_from_canopyinterception evaporationkg m-2 s-1upwardTYX1tranTranspirationvegetation transpirationkg m-2 s-1upwardTYX1esliquid_water_evaporation_flux_from_soilbare soil evaporationkg m-2 s-1upwardTYX2eowliquid_water_evaporation_flux_from_open_wateropen water evaporationkg m-2 s-1upwardTYX2esnliquid_water_evaporation_flux_from_surface_snowsnow evaporationkg m-2 s-1upwardTYX2sblsurface_snow_and_ice_sublimation_fluxsnow sublimationkg m-2 s-1upwardTYX2slbnosnsublimation_amount_assuming_no_snowsublimation of the snow free areakg m-2 s-1upwardTYX2potetwater_potential_evapotranspiration_fluxpotential evapotranspirationkg m-2 s-1upwardTYX1mrrorunoff_fluxtotal runoffkg m-2 s-1outTYX2mrrossurface_runoff_fluxsurface runoffkg m-2 s-1outTYX1mrrobsubsurface_runoff_fluxsubsurface runoffkg m-2 s-1outTYX1snmsurface_snow_and_ice_melt_fluxsnowmeltkg m-2 s-1solid to liquidTYX1snrefrsurface_snow_and_ice_refreezing_fluxrefreezing of water in the snowkg m-2 s-1liquid to solidTYX2snmslsurface_snow_melt_flux_into_soil_layerwater flowing out of snowpackkg m-2 s-1outTYX2qgwrwater_flux_from_soil_layer_to_groundwatergroundwater recharge fromkg m-2 s-1outTYXsoil layer2rivowater_flux_from_upstreamriver inflowm3 s-1inTYX2riviwater_flux_to_downstreamriver dischargem3 s-1outTYX1dslwchange_over_time_in_water_content_of_soil_layerchange in soil moisturekg m-2increaseTYX1dsnchange_over_time_in_surface_snow_and_ice_amountchange in snow water equivalentkg m-2increaseTYX1dswchange_over_time_in_surface_water_amountchange in surface water storagekg m-2increaseTYX1dcwchange_over_time_in_canopy_water_amountchange in interception storagekg m-2increaseTYX2dgwchange_over_time_in_groundwaterchange in groundwaterkg m-2increaseTYX2drivwchange_over_time_in_river_water_amountchange in river storagekg m-2increaseTYX1rzwcwater_content_of_root_zoneroot zone soil moisturekg m-2–TYX1cwcanopy_water_amounttotal canopy water storagekg m-2–TYX1snwsurface_snow_amountsnow water equivalentkg m-2–TZYX1snwccanopy_snow_amountSWE intercepted by the vegetationkg m-2–TYX2lwsnlliquid_water_content_of_snow_layerliquid water in snow packkg m-2–TZYX1swsurface_water_amount_assuming_no_snowsurface water storagekg m-2–TYX1mrlslmoisture_content_of_soil_layeraverage layer soil moisturekg m-2–TZYX1mrsosmoisture_content_of_soil_layermoisture in top soil (10cm) layerkg m-2–TYX1mrsowrelative_soil_moisture_content_above_field_capacitytotal soil wetness––TYX2wtddepth_of_soil_moisture_saturationwater table depthm–TYX1twscanopy_and_surface_and_subsurface_water_amountterrestrial water storagekg m-2–TYX2mrlqsomass_fraction_of_unfrozen_water_in_soil_layeraverage layer fraction of––TZYXliquid moisture1mrfsofrmass_fraction_of_frozen_water_in_soil_layeraverage layer fraction of––TZYXfrozen moisture2prrsnmass_fraction_of_rainfall_onto_snowfraction of rainfall on snow.––TYX2prsnsnmass_fraction_of_snowfall_onto_snowfraction of snowfall on snow.––TYX1lqsnmass_fraction_of_liquid_water_in_snowsnow liquid fraction––TZYX1sndsurface_snow_thicknessdepth of snow layerm–TYX1agesnoage_of_surface_snowsnow ageday–TYX2sootsnsoot_content_of_surface_snowsnow soot contentkg m-2–TYX2sicsea_ice_area_fractionice-covered fraction––TYX2sitsea_ice_thicknesssea-ice thicknessm–TYX2dfrdepth_of_frozen_soilfrozen soil depthmdownwardTYX2dmltdepth_of_subsurface_meltingdepth to soil thawmdownwardTYX2tpfpermafrost_layer_thicknesspermafrost layer thicknessm–TYX2pflwliquid_water_content_of_permafrost_layerliquid water content ofkg m-2–TYXpermafrost layeraerodynamic conductancem s-1-TYX2aresaerodynamic_resistanceaerodynamic resistances m-1–TYX
Continued.
p∗Namestandard_name (cf)long_name (netCDF)UnitDirectionDim.1nudgincwnudging_increment_of_total_waternudging increment of waterkg m-2increaseTYX1hurrelative_humidityrelative humidity%–TYX1hurmaxrelative_humidity_maximumdaily maximum near-surface%–TYXrelative humidity1hurminrelative_humidity_minimumdaily minimum near-surface%–TYXrelative humidity
Variable request table “LCmon”: monthly variables related to the
carbon cycle.
p∗Namestandard_name (cf)long_name (netCDF)UnitDirectionDim.1gppgross_primary_productivity_of_carbongross primary productionkg m-2 s-1downwardTYX1nppnet_primary_productivity_of_carbonnet primary productionkg m-2 s-1downwardTYX1nepsurface_net_downward_mass_flux_of_carbon_net ecosystem exchangekg m-2 s-1downwardTYXdioxide_expressed_as_carbon_due_to_all_land_processes_excluding_anthropogenic_land_use_change1raplant_respiration_carbon_fluxautotrophic respirationkg m-2 s-1upwardTYX1rhheterotrophic_respiration_carbon_fluxheterotrophic respirationkg m-2 s-1upwardTYX1fLucsurface_net_upward_mass_flux_of_carbon_net carbon mass flux intokg m-2 s-1upwardTYXdioxide_expressed_as_carbon_due_to_emission_atmosphere due to land usefrom_anthropogenic_land_use_changechange1cSoilsoil_carbon_contentcarbon mass in soil poolkg m-2–TYX1cLitterlitter_carbon_contentcarbon mass in litter poolkg m-2–TYX1cVegvegetation_carbon_contentcarbon mass in vegetationkg m-2–TYX1cProductcarbon_content_of_products_of_carbon mass in products ofkg m-2–TYXanthropogenic_land_use_changeland use change2cLeafleaf_carbon_contentcarbon mass in leaveskg m-2–TYX2cWoodwood_carbon_contentcarbon mass in woodkg m-2–TYX2cRootroot_carbon_contentcarbon mass in rootskg m-2–TYX2cMiscmiscellaneous_living_matter_carbon_contentcarbon mass in other livingkg m-2–TYXcompartments on land2fVegLitterlitter_carbon_fluxtotal carbon mass flux fromkg m-2 s-1–TYXvegetation to litter2fLitterSoilcarbon_mass_flux_into_soil_from_littertotal carbon mass flux fromkg m-2 s-1–TYXlitter to soil2fVegSoilcarbon_mass_flux_into_soil_from_total carbon mass flux fromkg m-2 s-1–TYXvegetation_excluding_littervegetation directly to soil1treeFracarea_fractiontree cover fraction%–TYX1grassFracarea_fractionnatural grass fraction%–TYX1shrubFracarea_fractionshrub fraction%–TYX1cropFracarea_fractioncrop fraction%–TYX1pastureFracarea_fractionanthropogenic pasture fraction%–TYX1baresoilFracarea_fractionbare soil fraction%–TYX1residualFracarea_fractionfraction of grid cell that is%–TYXland but neither vegetation-covered nor bare soil1laileaf_area_indexleaf area indexkg m-2–TYX
Variable request table “L3hr”: 3-hourly variables to generate the
atmospheric boundary conditions for the off-line simulation.
The authors thank the CMIP panel of the WCRP Working Group on Climate
Modelling for their efforts in coordinating the CMIP6 enterprise. Graham P. Weedon was
supported by the Joint UK DECC/Defra Met Office Hadley Climate Centre
Programme (GA01101). Jiafu Mao is supported by the Biogeochemistry-Climate
Feedbacks Scientific Focus Area project funded through the Regional and
Global Climate Modeling Program in Climate and Environmental Sciences
Division (CESD) of the Biological and Environmental Research (BER) Program
in the U.S. Department of Energy (DOE) Office of Science. Oak Ridge National
Laboratory is managed by UT-BATTELLE for DOE under contract
DE-AC05-00OR22725. H. Kim and T. Oki were supported by Japan Society for the Promotion of
Science KAKENHI (16H06291). Hanna Lee (NorESM) has expressed intention to participate
in LS3MIP when feasible, but has not contributed to this manuscript.
Edited by: J. Kala
Reviewed by: P. Dirmeyer, G. Abramowitz, and one anonymous referee
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