Evaluating the strength of the land–atmosphere moisture feedback in Earth system models using satellite observations

Abstract. The relationship between terrestrial water storage (TWS) and atmospheric processes has important implications for predictability of climatic extremes and projection of future climate change. In places where moisture availability limits evapotranspiration (ET), variability in TWS has the potential to influence surface energy fluxes and atmospheric conditions. Where atmospheric conditions, in turn, influence moisture availability, a full feedback loop exists. Here we developed a novel approach for measuring the strength of both components of this feedback loop, i.e., the forcing of the atmosphere by variability in TWS and the response of TWS to atmospheric variability, using satellite observations of TWS, precipitation, solar radiation, and vapor pressure deficit during 2002–2014. Our approach defines metrics to quantify the relationship between TWS anomalies and climate globally on a seasonal to interannual timescale. Metrics derived from the satellite data were used to evaluate the strength of the feedback loop in 38 members of the Community Earth System Model (CESM) Large Ensemble (LENS) and in six models that contributed simulations to phase 5 of the Coupled Model Intercomparison Project (CMIP5). We found that both forcing and response limbs of the feedback loop in LENS were stronger than in the satellite observations in tropical and temperate regions. Feedbacks in the selected CMIP5 models were not as strong as those found in LENS, but were still generally stronger than those estimated from the satellite measurements. Consistent with previous studies conducted across different spatial and temporal scales, our analysis suggests that models may overestimate the strength of the feedbacks between the land surface and the atmosphere. We describe several possible mechanisms that may contribute to this bias, and discuss pathways through which models may overestimate ET or overestimate the sensitivity of ET to TWS.

[1]  Pierre Gentine,et al.  Probability of afternoon precipitation in eastern United States and Mexico enhanced by high evaporation , 2011 .

[2]  Duane E. Waliser,et al.  Satellite Observations for CMIP5: The Genesis of Obs4MIPs , 2014 .

[3]  S. Kollet,et al.  The concept of dual‐boundary forcing in land surface‐subsurface interactions of the terrestrial hydrologic and energy cycles , 2014 .

[4]  Diego G. Miralles,et al.  Reconciling spatial and temporal soil moisture effects on afternoon rainfall , 2015, Nature Communications.

[5]  Daehyun Kim,et al.  MJO and Convectively Coupled Equatorial Waves Simulated by CMIP5 Climate Models , 2013 .

[6]  C. Tucker,et al.  Vegetation dynamics and rainfall sensitivity of the Amazon , 2014, Proceedings of the National Academy of Sciences.

[7]  Ronald,et al.  GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics , 2012 .

[8]  M. Lawrence.  The relationship between relative humidity and the dewpoint temperature in moist air - A simple conversion and applications , 2005 .

[9]  R. Koster,et al.  The Second Phase of the Global Land Atmosphere Coupling Experiment (GLACE-2): Impact of Land Initialization on Subseasonal Forecasts , 2010 .

[10]  S. Klein,et al.  Land‐atmosphere coupling manifested in warm‐season observations on the U.S. southern great plains , 2013 .

[11]  R.A.M. de Jeu,et al.  Soil moisture‐temperature coupling: A multiscale observational analysis , 2012 .

[12]  R. Dickinson,et al.  A Negative Soil Moisture-Precipitation Relationship and Its Causes , 2008 .

[13]  K. Findell.  Atmospheric Controls on Soil Moisture-Boundary Layer Interactions , 2001 .

[14]  Douglas C. Morton,et al.  How much global burned area can be forecast on seasonal time scales using sea surface temperatures? , 2016 .

[15]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[16]  R. Neale,et al.  The Mean Climate of the Community Atmosphere Model (CAM4) in Forced SST and Fully Coupled Experiments , 2013 .

[17]  P. Dirmeyer,et al.  Trends in Land–Atmosphere Interactions from CMIP5 Simulations , 2013 .

[18]  S. Hagemann,et al.  Contributions of soil moisture interactions to climate change in the tropics in the GLACE–CMIP5 experiment , 2015, Climate Dynamics.

[19]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[20]  Elfatih A. B. Eltahir,et al.  An analysis of the soil moisture‐rainfall feedback, based on direct observations from Illinois , 1997 .

[21]  D. Lawrence,et al.  Regions of Strong Coupling Between Soil Moisture and Precipitation , 2004, Science.

[22]  Diego G. Miralles,et al.  Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation , 2014 .

[23]  J. Randerson,et al.  Satellite observations of terrestrial water storage provide early warning information about drought and fire season severity in the Amazon , 2013 .

[24]  Victor Zlotnicki,et al.  Time‐variable gravity from GRACE: First results , 2004 .

[25]  C. Taylor,et al.  Afternoon rain more likely over drier soils , 2012, Nature.

[26]  Taikan Oki,et al.  Role of rivers in the seasonal variations of terrestrial water storage over global basins , 2009 .

[27]  J. Randerson,et al.  Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity , 2016, Proceedings of the National Academy of Sciences.

[28]  Wouter Dorigo,et al.  Using remotely sensed soil moisture for land-atmosphere coupling diagnostics: the role of surface vs. root-zone soil moisture variability. , 2014 .

[29]  Taikan Oki,et al.  GLACE: The Global Land–Atmosphere Coupling Experiment. Part II: Analysis , 2006, Journal of Hydrometeorology.

[30]  Ying Gao,et al.  The Soil Moisture Active Passive Experiments (SMAPEx): Toward Soil Moisture Retrieval From the SMAP Mission , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[31]  V. Brovkin,et al.  Impact of soil moisture‐climate feedbacks on CMIP5 projections: First results from the GLACE‐CMIP5 experiment , 2013 .

[32]  Joel Susskind,et al.  Improved methodology for surface and atmospheric soundings, error estimates, and quality control procedures: the atmospheric infrared sounder science team version-6 retrieval algorithm , 2014 .

[33]  A. Scott Denning,et al.  Impact of Evapotranspiration on Dry Season Climate in the Amazon Forest , 2014 .

[34]  Naota Hanasaki,et al.  GSWP-2 Multimodel Analysis and Implications for Our Perception of the Land Surface , 2006 .

[35]  Jakob Zscheischler,et al.  Impact of soil moisture on extreme maximum temperatures in Europe , 2015 .

[36]  Guiling Wang,et al.  Summer Land–Atmosphere Coupling Strength in the United States: Comparison among Observations, Reanalysis Data, and Numerical Models , 2012 .

[37]  S. Seneviratne,et al.  Land–atmosphere coupling and climate change in Europe , 2006, Nature.

[38]  Devon E. Worth,et al.  Climate coupling between temperature, humidity, precipitation, and cloud cover over the Canadian Prairies , 2014 .

[39]  Jean-Charles Dupont,et al.  Combined influence of atmospheric physics and soil hydrology on the simulated meteorology at the SIRTA atmospheric observatory , 2013, Climate Dynamics.

[40]  Xubin Zeng,et al.  Comparison of Land–Precipitation Coupling Strength Using Observations and Models , 2009 .

[41]  S. Bony,et al.  Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5 , 2013, Climate Dynamics.

[42]  Frédéric Hourdin,et al.  Role of clouds and land‐atmosphere coupling in midlatitude continental summer warm biases and climate change amplification in CMIP5 simulations , 2014 .

[43]  Michael Notaro,et al.  Statistical identification of global hot spots in soil moisture feedbacks among IPCC AR4 models , 2008 .

[44]  S. Seneviratne,et al.  Systematic land climate and evapotranspiration biases in CMIP5 simulations , 2014, Geophysical research letters.

[45]  D. Lawrence,et al.  Parameterization improvements and functional and structural advances in Version 4 of the Community Land Model , 2011 .

[46]  David R. Doelling,et al.  Toward Optimal Closure of the Earth's Top-of-Atmosphere Radiation Budget , 2009 .

[47]  Elfatih A. B. Eltahir,et al.  A Soil Moisture–Rainfall Feedback Mechanism: 1. Theory and observations , 1998 .

[48]  K.,et al.  The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability , 2015 .

[49]  D. Lawrence,et al.  GLACE: The Global Land-Atmosphere Coupling Experiment. Part I: Overview , 2006 .

[50]  P. Claps,et al.  An Analysis of the Soil Moisture Feedback on Convective and Stratiform Precipitation , 2008 .

[51]  Randal D. Koster,et al.  Do Global Models Properly Represent the Feedback between Land and Atmosphere? , 2006, Journal of Hydrometeorology.

[52]  George C. Hurtt,et al.  Carbon cycling under 300 years of land use change: Importance of the secondary vegetation sink , 2009 .

[53]  Yann Kerr,et al.  ESA's Soil Moisture and Ocean Salinity Mission: Mission Performance and Operations , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[54]  Paolo De Angelis,et al.  Reconciling the optimal and empirical approaches to modelling stomatal conductance , 2011 .

[55]  Randal D. Koster,et al.  GLACE: The Global Land-Atmosphere Coupling Experiment , 2018 .

[56]  Sonia I. Seneviratne,et al.  Observational evidence for soil-moisture impact on hot extremes in southeastern Europe , 2011 .

[57]  Guiling Wang,et al.  Impact of Sea Surface Temperature and Soil Moisture on Summer Precipitation in the United States Based on Observational Data , 2011 .

[58]  F. Landerer,et al.  Accuracy of scaled GRACE terrestrial water storage estimates , 2012 .

[59]  K. Findell,et al.  Data Length Requirements for Observational Estimates of Land–Atmosphere Coupling Strength , 2015 .

[60]  Pierre Gentine,et al.  Interannual Coupling between Summertime Surface Temperature and Precipitation over Land: Processes and Implications for Climate Change* , 2015 .

[61]  Victor Zlotnicki,et al.  Title Time-variable gravity from GRACE : First results Permalink , 2004 .

[62]  S. Seneviratne,et al.  Statistical Analyses of Land–Atmosphere Feedbacks and Their Possible Pitfalls , 2010 .

[63]  A. Dai.  Precipitation Characteristics in Eighteen Coupled Climate Models , 2006 .

[64]  David M. Lawrence,et al.  A GRACE‐based assessment of interannual groundwater dynamics in the Community Land Model , 2015 .

[65]  Christa D. Peters-Lidard,et al.  A Modeling and Observational Framework for Diagnosing Local Land–Atmosphere Coupling on Diurnal Time Scales , 2009 .

[66]  Alan K. Betts,et al.  Land‐Surface‐Atmosphere Coupling in Observations and Models , 2009 .

[67]  Mathew Barlow,et al.  The NAO, the AO, and Global Warming: How Closely Related? , 2005 .

[68]  Samanpreet Kaur,et al.  Climate Change Projections , 2018 .

[69]  C. Bretherton,et al.  The Soil Moisture–Precipitation Feedback in Simulations with Explicit and Parameterized Convection , 2009 .

[70]  Lifeng Luo,et al.  Contribution of land surface initialization to subseasonal forecast skill: First results from a multi‐model experiment , 2010 .

[71]  David T. Bolvin,et al.  Improving the global precipitation record: GPCP Version 2.1 , 2009 .

[72]  Marie-Alice Foujols,et al.  Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model , 2013, Climate Dynamics.

[73]  I. Fung,et al.  Root functioning modifies seasonal climate. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[74]  M. Hoerling,et al.  Atmosphere and Ocean Origins of North American Droughts , 2014 .

[75]  David M. Lawrence,et al.  Assessing a dry surface layer‐based soil resistance parameterization for the Community Land Model using GRACE and FLUXNET‐MTE data , 2014 .

[76]  Eric F. Wood,et al.  A Global Intercomparison of Modeled and Observed Land–Atmosphere Coupling* , 2012 .

[77]  W. G. Strand,et al.  Climate Change Projections in CESM1(CAM5) Compared to CCSM4 , 2013 .

[78]  Pierre Gentine,et al.  Land-surface controls on afternoon precipitation diagnosed from observational data : uncertainties and confounding factors , 2014 .

[79]  S. Seneviratne,et al.  Investigating soil moisture-climate interactions in a changing climate: A review , 2010 .

[80]  David A. Randall,et al.  Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model , 2016 .

[81]  P. Dirmeyer,et al.  Interannual Variability of Land-Atmosphere Coupling Strength , 2013 .

[82]  C. Taylor,et al.  Modeling soil moisture‐precipitation feedback in the Sahel: Importance of spatial scale versus convective parameterization , 2013 .

[83]  Effect of land‐atmosphere coupling strength on impacts from Amazonian deforestation , 2014 .

[84]  Lifeng Luo,et al.  The Second Phase of the Global Land–Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill , 2011 .

[85]  Peter E. Thornton,et al.  Preindustrial-Control and Twentieth-Century Carbon Cycle Experiments with the Earth System Model CESM1(BGC) , 2014 .

[86]  Frank Flechtner,et al.  Status of the GRACE Follow-On Mission , 2013 .