Editorial Type:
Article
Article Type:
Research Article

Understanding Responses of Summer Continental Daily Temperature Variance to Perturbations in the Land Surface Evaporative Resistance

Wenwen Kong aInstitute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, California

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https://orcid.org/0000-0003-2999-8081
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Karen A. McKinnon aInstitute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, California
bDepartments of Statistics and Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Isla R. Simpson cClimate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Marysa M. Laguë dColdwater Lab, University of Saskatchewan Center for Hydrology, Canmore, Alberta, Canada

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Online Publication:
16 Feb 2023
Print Publication:
15 Mar 2023
DOI:
https://doi.org/10.1175/JCLI-D-21-1011.1
Page(s):
1653–1678
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Abstract

Understanding the roles of land surface conditions and atmospheric circulation on continental daily temperature variance is key to improving predictions of temperature extremes. Evaporative resistance (rs, hereafter), a function of the land cover type, reflects the ease with which water can be evaporated or transpired and is a strong control on land–atmosphere interactions. This study explores the effects of rs perturbations on summer daily temperature variance using the Simple Land Interface Model (SLIM) by mimicking, for rs only, a global land cover conversion from forest to crop/grassland. Decreasing rs causes a global cooling. The cooling is larger in wetter areas and weaker in drier areas, and primarily results from perturbations in shortwave radiation (SW) and latent heat flux (LH). Decreasing rs enhances cloud cover due to greater land surface evaporation and thus reduces incoming SW over most land areas. When rs decreases, wetter areas experience strong evaporative cooling, while drier areas become more moisture-limited and thus experience less cooling. Thermal advection further shapes the temperature response by damping the combined impacts of SW and LH. Temperature variance increases in drier areas and decreases in wetter areas as rs decreases. The temperature variance changes can be largely explained from changes in the combined variance of SW and LH, including an important contribution of changes in the covariance of SW and LH. In contrast, the effects of changes in thermal advection variance mainly affect the Northern Hemisphere midlatitudes.

Significance Statement

This study aims to better understand processes governing daily near-surface air temperature variance over land. We use an idealized modeling framework to explore the effects of land surface evaporative resistance (a parameter that controls how hard it is to evaporate water from the surface) on summer daily temperature variance. We find that a uniform decrease of evaporative resistance across the global land surface causes changes in the temperature variance that can be predicted from changes in the combined variance of shortwave radiation and latent heat flux. The variance of horizontal advection is important in altering the temperature variance in the Northern Hemisphere midlatitudes. Our findings shed light on predicting the characteristics of temperature variability as a function of surface conditions.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wenwen Kong, wenwen.kong@berkeley.edu

Abstract

Understanding the roles of land surface conditions and atmospheric circulation on continental daily temperature variance is key to improving predictions of temperature extremes. Evaporative resistance (rs, hereafter), a function of the land cover type, reflects the ease with which water can be evaporated or transpired and is a strong control on land–atmosphere interactions. This study explores the effects of rs perturbations on summer daily temperature variance using the Simple Land Interface Model (SLIM) by mimicking, for rs only, a global land cover conversion from forest to crop/grassland. Decreasing rs causes a global cooling. The cooling is larger in wetter areas and weaker in drier areas, and primarily results from perturbations in shortwave radiation (SW) and latent heat flux (LH). Decreasing rs enhances cloud cover due to greater land surface evaporation and thus reduces incoming SW over most land areas. When rs decreases, wetter areas experience strong evaporative cooling, while drier areas become more moisture-limited and thus experience less cooling. Thermal advection further shapes the temperature response by damping the combined impacts of SW and LH. Temperature variance increases in drier areas and decreases in wetter areas as rs decreases. The temperature variance changes can be largely explained from changes in the combined variance of SW and LH, including an important contribution of changes in the covariance of SW and LH. In contrast, the effects of changes in thermal advection variance mainly affect the Northern Hemisphere midlatitudes.

Significance Statement

This study aims to better understand processes governing daily near-surface air temperature variance over land. We use an idealized modeling framework to explore the effects of land surface evaporative resistance (a parameter that controls how hard it is to evaporate water from the surface) on summer daily temperature variance. We find that a uniform decrease of evaporative resistance across the global land surface causes changes in the temperature variance that can be predicted from changes in the combined variance of shortwave radiation and latent heat flux. The variance of horizontal advection is important in altering the temperature variance in the Northern Hemisphere midlatitudes. Our findings shed light on predicting the characteristics of temperature variability as a function of surface conditions.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wenwen Kong, wenwen.kong@berkeley.edu

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  • Andrade, C., S. M. Leite, and J. A. Santos, 2012: Temperature extremes in Europe: Overview of their driving atmospheric patterns. Nat. Hazards Earth Syst. Sci., 12, 1671–1691, https://doi.org/10.5194/nhess-12-1671-2012.

    • Search Google Scholar
    • Export Citation

  • Baldwin, J. W., J. B. Dessy, G. A. Vecchi, and M. Oppenheimer, 2019: Temporally compound heat wave events and global warming: An emerging hazard. Earth’s Future, 7, 411–427, https://doi.org/10.1029/2018EF000989.

    • Search Google Scholar
    • Export Citation

  • Berg, A., B. R. Lintner, K. L. Findell, S. Malyshev, P. C. Loikith, and P. Gentine, 2014: Impact of soil moisture–atmosphere interactions on surface temperature distribution. J. Climate, 27, 7976–7993, https://doi.org/10.1175/JCLI-D-13-00591.1.

    • Search Google Scholar
    • Export Citation

  • Berg, A., and Coauthors, 2015: Interannual coupling between summertime surface temperature and precipitation over land: Processes and implications for climate change. J. Climate, 28, 1308–1328, https://doi.org/10.1175/JCLI-D-14-00324.1.

    • Search Google Scholar
    • Export Citation

  • Bogenschutz, P. A., A. O. Gettelman, C. Hannay, V. E. Larson, R. B. Neale, C. Craig, and C.-C. Chen, 2018: The path to CAM6: Coupled simulations with CAM5.4 and CAM5.5. Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018.

    • Search Google Scholar
    • Export Citation

  • Bonan, G. B., 1996: A land surface model (LSM version 1.0) for ecological, hydrological, and atmospheric studies: Technical description and user’s guide. NCAR Tech. Note NCAR/TN-417+STR, 150 pp., https://doi.org/10.5065/D6DF6P5X.

  • Bonan, G. B., 2016: Ecological Climatology. 3rd ed. Cambridge University Press, 692 pp., https://doi.org/10.1017/CBO9781107339200.

  • Branstator, G., and H. Teng, 2017: Tropospheric waveguide teleconnections and their seasonality. J. Atmos. Sci., 74, 1513–1532, https://doi.org/10.1175/JAS-D-16-0305.1.

    • Search Google Scholar
    • Export Citation

  • Bretherton, C. S., 2014: Lecture 3: Statistical sampling uncertainty. University of Washington, 5 pp., https://atmos.washington.edu/∼breth/classes/AM582/lect/lect3-notes.pdf.

  • Budyko, M. I., 1961: The heat balance of the Earth’s surface. Sov. Geogr., 2, 3–13, https://doi.org/10.1080/00385417.1961.10770761.

  • Canadell, J. G., and M. R. Raupach, 2008: Managing forests for climate change mitigation. Science, 320, 1456–1457, https://doi.org/10.1126/science.1155458.

    • Search Google Scholar
    • Export Citation

  • Chan, D., A. Cobb, L. R. V. Zeppetello, D. S. Battisti, and P. Huybers, 2020: Summertime temperature variability increases with local warming in midlatitude regions. Geophys. Res. Lett., 47, e2020GL087624, https://doi.org/10.1029/2020GL087624.

    • Search Google Scholar
    • Export Citation

  • Cheruy, F., J. L. Dufresne, F. Hourdin, and A. Ducharne, 2014: Role of clouds and land–atmosphere coupling in midlatitude continental summer warm biases and climate change amplification in CMIP5 simulations. Geophys. Res. Lett., 41, 6493–6500, https://doi.org/10.1002/2014GL061145.

    • Search Google Scholar
    • Export Citation

  • Cheruy, F., J. L. Dufresne, S. A. Mesbah, J. Y. Grandpeix, and F. Wang, 2017: Role of soil thermal inertia in surface temperature and soil moisture–temperature feedback. J. Adv. Model. Earth Syst., 9, 2906–2919, https://doi.org/10.1002/2017MS001036.

    • Search Google Scholar
    • Export Citation

  • Collins, W. D., and Coauthors, 2004: Description of the NCAR Community Atmosphere Model (CAM 3.0). NCAR Tech. Note NCAR/TN-464+STR, 214 pp., https://doi.org/10.5065/D63N21CH.

  • Community Earth System Model developers and affiliates, 2019: f.e21.FHIST_bgc.f09_f09_mg17.CMIP6-AMIP.001 CMIP6 CESM2 AMIP hindcast (1950–2014) with interactive land (CLM5), data ocean, prescribed sea ice, and non-evolving land ice (CISM2.1). UCAR/NCAR-Climate and Global Dynamics Laboratory, accessed 7 November 2020, https://www.earthsystemgrid.org/dataset/ucar.cgd.cesm2.f.e21.FHIST_BGC.f09_f09_mg17.CMIP6-AMIP.001.html.

  • Compo, G. P., and P. D. Sardeshmukh, 2009: Oceanic influences on recent continental warming. Climate Dyn., 32, 333–342, https://doi.org/10.1007/s00382-008-0448-9.

    • Search Google Scholar
    • Export Citation

  • Cowan, T., G. C. Hegerl, I. Colfescu, M. Bollasina, A. Purich, and G. Boschat, 2017: Factors contributing to record-breaking heat waves over the great plains during the 1930s dust bowl. J. Climate, 30, 2437–2461, https://doi.org/10.1175/JCLI-D-16-0436.1.

    • Search Google Scholar
    • Export Citation

  • Danabasoglu, G., and Coauthors, 2020: The Community Earth System Model version 2 (CESM2). J. Adv. Model. Earth Syst., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916.

    • Search Google Scholar
    • Export Citation

  • Davin, E. L., and N. de Noblet-Ducoudré, 2010: Climatic impact of global-scale deforestation: Radiative versus nonradiative processes. J. Climate, 23, 97–112, https://doi.org/10.1175/2009JCLI3102.1.

    • Search Google Scholar
    • Export Citation

  • Delworth, T., and S. Manabe, 1989: The influence of soil wetness on near-surface atmospheric variability. J. Climate, 2, 1447–1462, https://doi.org/10.1175/1520-0442(1989)002<1447:TIOSWO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • de Noblet-Ducoudré, N., and Coauthors, 2012: Determining robust impacts of land-use-induced land cover changes on surface climate over North America and Eurasia: Results from the first set of LUCID experiments. J. Climate, 25, 3261–3281, https://doi.org/10.1175/JCLI-D-11-00338.1.

    • Search Google Scholar
    • Export Citation

  • Devaraju, N., N. de Noblet-Ducoudré, B. Quesada, and G. Bala, 2018: Quantifying the relative importance of direct and indirect biophysical effects of deforestation on surface temperature and teleconnections. J. Climate, 31, 3811–3829, https://doi.org/10.1175/JCLI-D-17-0563.1.

    • Search Google Scholar
    • Export Citation

  • Dirmeyer, P. A., 2003: The role of the land surface background state in climate predictability. J. Hydrometeor., 4, 599–610, https://doi.org/10.1175/1525-7541(2003)004<0599:TROTLS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Findell, K. L., E. Shevliakova, P. C. D. Milly, and R. J. Stouffer, 2007: Modeled impact of anthropogenic land cover change on climate. J. Climate, 20, 3621–3634, https://doi.org/10.1175/JCLI4185.1.

    • Search Google Scholar
    • Export Citation

  • Fischer, E. M., and R. Knutti, 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Climate Change, 5, 560–564, https://doi.org/10.1038/nclimate2617.

    • Search Google Scholar
    • Export Citation

  • Fischer, E. M., and C. Schär, 2009: Future changes in daily summer temperature variability: Driving processes and role for temperature extremes. Climate Dyn., 33, 917, https://doi.org/10.1007/s00382-008-0473-8.

    • Search Google Scholar
    • Export Citation

  • Fischer, E. M., S. I. Seneviratne, D. Lüthi, and C. Schär, 2007a: Contribution of land–atmosphere coupling to recent European summer heat waves. Geophys. Res. Lett., 34, L06707, https://doi.org/10.1029/2006GL029068.

    • Search Google Scholar
    • Export Citation

  • Fischer, E. M., S. I. Seneviratne, P. L. Vidale, D. Lüthi, and C. Schär, 2007b: Soil moisture–atmosphere interactions during the 2003 European summer heat wave. J. Climate, 20, 5081–5099, https://doi.org/10.1175/JCLI4288.1.

    • Search Google Scholar
    • Export Citation

  • Garfinkel, C. I., and N. Harnik, 2017: The non-Gaussianity and spatial asymmetry of temperature extremes relative to the storm track: The role of horizontal advection. J. Climate, 30, 445–464, https://doi.org/10.1175/JCLI-D-15-0806.1.

    • Search Google Scholar
    • Export Citation

  • Gregory, J. M., and J. F. B. Mitchell, 1995: Simulation of daily variability of surface temperature and precipitation over Europe in the current and 2 × CO2 climates using the UKMO climate model. Quart. J. Roy. Meteor. Soc., 121, 1451–1476, https://doi.org/10.1002/qj.49712152611.

    • Search Google Scholar
    • Export Citation

  • Guo, Z., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part II: Analysis. J. Hydrometeor., 7, 611–625, https://doi.org/10.1175/JHM511.1.

    • Search Google Scholar
    • Export Citation

  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation

  • Hirschi, M., and Coauthors, 2011: Observational evidence for soil-moisture impact on hot extremes in southeastern Europe. Nat. Geosci., 4, 17–21, https://doi.org/10.1038/ngeo1032.

    • Search Google Scholar
    • Export Citation

  • Holmes, C. R., T. Woollings, E. Hawkins, and H. de Vries, 2016: Robust future changes in temperature variability under greenhouse gas forcing and the relationship with thermal advection. J. Climate, 29, 2221–2236, https://doi.org/10.1175/JCLI-D-14-00735.1.

    • Search Google Scholar
    • Export Citation

  • Hulley, G. C., B. Dousset, and B. H. Kahn, 2020: Rising trends in heatwave metrics across southern California. Earth’s Future, 8, e2020EF001480, https://doi.org/10.1029/2020EF001480.

    • Search Google Scholar
    • Export Citation

  • Hutcheon, J. A., A. Chiolero, and J. A. Hanley, 2010: Random measurement error and regression dilution bias. BMJ, 340, c2289, https://doi.org/10.1136/bmj.c2289.

    • Search Google Scholar
    • Export Citation

  • Jaeger, E. B., and S. I. Seneviratne, 2011: Impact of soil moisture–atmosphere coupling on European climate extremes and trends in a regional climate model. Climate Dyn., 36, 1919–1939, https://doi.org/10.1007/s00382-010-0780-8.

    • Search Google Scholar
    • Export Citation

  • Jeevanjee, N., P. Hassanzadeh, S. Hill, and A. Sheshadri, 2017: A perspective on climate model hierarchies. J. Adv. Model. Earth Syst., 9, 1760–1771, https://doi.org/10.1002/2017MS001038.

    • Search Google Scholar
    • Export Citation

  • Katz, R. W., and B. G. Brown, 1992: Extreme events in a changing climate: Variability is more important than averages. Climatic Change, 21, 289–302, https://doi.org/10.1007/BF00139728.

    • Search Google Scholar
    • Export Citation

  • Kim, J. E., M. M. Laguë, S. Pennypacker, E. Dawson, and A. L. S. Swann, 2020: Evaporative resistance is of equal importance as surface albedo in high-latitude surface temperatures due to cloud feedbacks. Geophys. Res. Lett., 47, e2019GL085663, https://doi.org/10.1029/2019GL085663.

    • Search Google Scholar
    • Export Citation

  • Klein, S. A., 1997: Synoptic variability of low-cloud properties and meteorological parameters in the subtropical trade wind boundary layer. J. Climate, 10, 2018–2039, https://doi.org/10.1175/1520-0442(1997)010<2018:SVOLCP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kooperman, G. J., M. S. Pritchard, T. A. O’Brien, and B. W. Timmermans, 2018: Rainfall from resolved rather than parameterized processes better represents the present-day and climate change response of moderate rates in the Community Atmosphere Model. J. Adv. Model. Earth Syst., 10, 971–988, https://doi.org/10.1002/2017MS001188.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part I: Overview. J. Hydrometeor., 7, 590–610, https://doi.org/10.1175/JHM510.1.

    • Search Google Scholar
    • Export Citation

  • Kotz, M., L. Wenz, and A. Levermann, 2021: Footprint of greenhouse forcing in daily temperature variability. Proc. Natl. Acad. Sci. USA, 118, e2103294118, https://doi.org/10.1073/pnas.2103294118.

    • Search Google Scholar
    • Export Citation

  • Laguë, M. M., and A. L. S. Swann, 2016: Progressive midlatitude afforestation: Impacts on clouds, global energy transport, and precipitation. J. Climate, 29, 5561–5573, https://doi.org/10.1175/JCLI-D-15-0748.1.

    • Search Google Scholar
    • Export Citation

  • Laguë, M. M., G. B. Bonan, and A. L. S. Swann, 2019: Separating the impact of individual land surface properties on the terrestrial surface energy budget in both the coupled and uncoupled land–atmosphere system. J. Climate, 32, 5725–5744, https://doi.org/10.1175/JCLI-D-18-0812.1.

    • Search Google Scholar
    • Export Citation

  • Laguë, M. M., A. L. S. Swann, and W. R. Boos, 2021: Radiative feedbacks on land surface change and associated tropical precipitation shifts. J. Climate, 34, 6651–6672, https://doi.org/10.1175/JCLI-D-20-0883.1.

    • Search Google Scholar
    • Export Citation

  • Lawrence, D. M., and Coauthors, 2019: The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst., 11, 4245–4287, https://doi.org/10.1029/2018MS001583.

    • Search Google Scholar
    • Export Citation

  • Lee, X., and Coauthors, 2011: Observed increase in local cooling effect of deforestation at higher latitudes. Nature, 479, 384–387, https://doi.org/10.1038/nature10588.

    • Search Google Scholar
    • Export Citation

  • Lenderink, G., A. van Ulden, B. van den Hurk, and E. van Meijgaard, 2007: Summertime inter-annual temperature variability in an ensemble of regional model simulations: Analysis of the surface energy budget. Climatic Change, 81, 233–247, https://doi.org/10.1007/s10584-006-9229-9.

    • Search Google Scholar
    • Export Citation

  • Linz, M., G. Chen, and Z. Hu, 2018: Large-scale atmospheric control on non-Gaussian tails of midlatitude temperature distributions. Geophys. Res. Lett., 45, 9141–9149, https://doi.org/10.1029/2018GL079324.

    • Search Google Scholar
    • Export Citation

  • Linz, M., G. Chen, B. Zhang, and P. Zhang, 2020: A framework for understanding how dynamics shape temperature distributions. Geophys. Res. Lett., 47, e2019GL085684, https://doi.org/10.1029/2019GL085684.

    • Search Google Scholar
    • Export Citation

  • Lohmann, U., and J. Feichter, 2005: Global indirect aerosol effects: A review. Atmos. Chem. Phys., 5, 715–737, https://doi.org/10.5194/acp-5-715-2005.

    • Search Google Scholar
    • Export Citation

  • Lorenz, R., E. L. Davin, and S. I. Seneviratne, 2012: Modeling land–climate coupling in Europe: Impact of land surface representation on climate variability and extremes. J. Geophys. Res., 117, D20109, https://doi.org/10.1029/2012JD017755.

    • Search Google Scholar
    • Export Citation

  • Luyssaert, S., and Coauthors, 2014: Land management and land-cover change have impacts of similar magnitude on surface temperature. Nat. Climate Change, 4, 389–393, https://doi.org/10.1038/nclimate2196.

    • Search Google Scholar
    • Export Citation

  • Ma, H.-Y., and Coauthors, 2014: On the correspondence between mean forecast errors and climate errors in CMIP5 models. J. Climate, 27, 1781–1798, https://doi.org/10.1175/JCLI-D-13-00474.1.

    • Search Google Scholar
    • Export Citation

  • Ma, H.-Y., and Coauthors, 2018: CAUSES: On the role of surface energy budget errors to the warm surface air temperature error over the central United States. J. Geophys. Res. Atmos., 123, 2888–2909, https://doi.org/10.1002/2017JD027194.

    • Search Google Scholar
    • Export Citation

  • Manabe, S., 1969: Climate and the ocean circulation. I. The atmospheric circulation and the hydrology of the Earth’s surface. Mon. Wea. Rev., 97, 739–774, https://doi.org/10.1175/1520-0493(1969)097<0739:CATOC>2.3.CO;2.

    • Search Google Scholar
    • Export Citation

  • Martens, B., and Coauthors, 2017: GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017.

    • Search Google Scholar
    • Export Citation

  • Martens, B., D. L. Schumacher, H. Wouters, J. Muñoz-Sabater, N. E. C. Verhoest, and D. G. Miralles, 2020: Evaluating the land-surface energy partitioning in ERA5. Geosci. Model Dev., 13, 4159–4181, https://doi.org/10.5194/gmd-13-4159-2020.

    • Search Google Scholar
    • Export Citation

  • Meehl, G. A., and C. Tebaldi, 2004: More intense, more frequent, and longer lasting heat waves in the 21st century. Science, 305, 994–997, https://doi.org/10.1126/science.1098704.

    • Search Google Scholar
    • Export Citation

  • Merrifield, A. L., and S.-P. Xie, 2016: Summer U.S. surface air temperature variability: Controlling factors and AMIP simulation biases. J. Climate, 29, 5123–5139, https://doi.org/10.1175/JCLI-D-15-0705.1.

    • Search Google Scholar
    • Export Citation

  • Milly, P. C. D., and A. B. Shmakin, 2002: Global modeling of land water and energy balances. Part I: The Land Dynamics (LaD) model. J. Hydrometeor., 3, 283–299, https://doi.org/10.1175/1525-7541(2002)003<0283:GMOLWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Miralles, D. G., T. R. H. Holmes, R. M. De Jeu, J. H. Gash, A. G. C. A. Meesters, and A. J. Dolman, 2011: Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011.

    • Search Google Scholar
    • Export Citation

  • Mu, Q., M. Zhao, and S. W. Running, 2011: Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ., 115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019.

    • Search Google Scholar
    • Export Citation

  • Mueller, B., and S. I. Seneviratne, 2014: Systematic land climate and evapotranspiration biases in CMIP5 simulations. Geophys. Res. Lett., 41, 128–134, https://doi.org/10.1002/2013GL058055.

    • Search Google Scholar
    • Export Citation

  • Mueller, N. D., E. E. Butler, K. A. McKinnon, A. Rhines, M. Tingley, N. M. Holbrook, and P. Huybers, 2016: Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Climate Change, 6, 317–322, https://doi.org/10.1038/nclimate2825.

    • Search Google Scholar
    • Export Citation

  • Norris, J. R., 1998: Low cloud type over the ocean from surface observations. Part I: Relationship to surface meteorology and the vertical distribution of temperature and moisture. J. Climate, 11, 369–382, https://doi.org/10.1175/1520-0442(1998)011<0369:LCTOTO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Panwar, A., A. Kleidon, and M. Renner, 2019: Do surface and air temperatures contain similar imprints of evaporative conditions? Geophys. Res. Lett., 46, 3802–3809, https://doi.org/10.1029/2019GL082248.

    • Search Google Scholar
    • Export Citation

  • Pfahl, S., and H. Wernli, 2012: Quantifying the relevance of atmospheric blocking for co-located temperature extremes in the Northern Hemisphere on (sub-) daily time scales. Geophys. Res. Lett., 39, L12807, https://doi.org/10.1029/2012GL052261.

    • Search Google Scholar
    • Export Citation

  • Pitman, A. J., 2003: The evolution of, and revolution in, land surface schemes designed for climate models. Int. J. Climatol., 23, 479–510, https://doi.org/10.1002/joc.893.

    • Search Google Scholar
    • Export Citation

  • Pitman, A. J., and Coauthors, 2009: Uncertainties in climate responses to past land cover change: First results from the LUCID intercomparison study. Geophys. Res. Lett., 36, https://doi.org/10.1029/2009GL039076.

    • Search Google Scholar
    • Export Citation

  • Purdy, A. J., J. B. Fisher, M. L. Goulden, and J. S. Famiglietti, 2016: Ground heat flux: An analytical review of 6 models evaluated at 88 sites and globally. J. Geophys. Res. Biogeosci., 121, 3045–3059, https://doi.org/10.1002/2016JG003591.

    • Search Google Scholar
    • Export Citation

  • Quesada, B., R. Vautard, P. Yiou, M. Hirschi, and S. I. Seneviratne, 2012: Asymmetric European summer heat predictability from wet and dry southern winters and springs. Nat. Climate Change, 2, 736–741, https://doi.org/10.1038/nclimate1536.

    • Search Google Scholar
    • Export Citation

  • Rasmijn, L. M., G. van der Schrier, R. Bintanja, J. Barkmeijer, A. Sterl, and W. Hazeleger, 2018: Future equivalent of 2010 Russian heatwave intensified by weakening soil moisture constraints. Nat. Climate Change, 8, 381–385, https://doi.org/10.1038/s41558-018-0114-0.

    • Search Google Scholar
    • Export Citation

  • Ruffault, J., and Coauthors, 2020: Increased likelihood of heat-induced large wildfires in the Mediterranean Basin. Sci. Rep., 10, 13790, https://doi.org/10.1038/s41598-020-70069-z.

    • Search Google Scholar
    • Export Citation

  • Schär, C., P. L. Vidale, D. Lüthi, C. Frei, C. Häberli, M. A. Liniger, and C. Appenzeller, 2004: The role of increasing temperature variability in European summer heatwaves. Nature, 427, 332–336, https://doi.org/10.1038/nature02300.

    • Search Google Scholar
    • Export Citation

  • Schneider, T., T. Bischoff, and H. Płotka, 2015: Physics of changes in synoptic midlatitude temperature variability. J. Climate, 28, 2312–2331, https://doi.org/10.1175/JCLI-D-14-00632.1.

    • Search Google Scholar
    • Export Citation

  • Schwingshackl, C., M. Hirschi, and S. I. Seneviratne, 2018: Global contributions of incoming radiation and land surface conditions to maximum near-surface air temperature variability and trend. Geophys. Res. Lett., 45, 5034–5044, https://doi.org/10.1029/2018GL077794.

    • Search Google Scholar
    • Export Citation

  • Screen, J. A., 2014: Arctic amplification decreases temperature variance in northern mid- to high-latitudes. Nat. Climate Change, 4, 577–582, https://doi.org/10.1038/nclimate2268.

    • Search Google Scholar
    • Export Citation

  • Seneviratne, S. I., D. Lüthi, M. Litschi, and C. Schär, 2006 : Land–atmosphere coupling and climate change in Europe. Nature, 443, 205–209, https://doi.org/10.1038/nature05095.

    • Search Google Scholar
    • Export Citation

  • Seneviratne, S. I., T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J. Teuling, 2010: Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004.

    • Search Google Scholar
    • Export Citation

  • Seneviratne, S. I., and Coauthors, 2012: Changes in climate extremes and their impacts on the natural physical environment. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, C. B. Field et al., Eds., Cambridge University Press, 109–230, https://doi.org/10.7916/d8-6nbt-s431.

  • Seneviratne, S. I., and Coauthors, 2013: Impact of soil moisture–climate feedbacks on CMIP5 projections: First results from the GLACE-CMIP5 experiment. Geophys. Res. Lett., 40, 5212–5217, https://doi.org/10.1002/grl.50956.

    • Search Google Scholar
    • Export Citation

  • Singh, N., A. Mhawish, S. Ghosh, T. Banerjee, and R. K. Mall, 2019: Attributing mortality from temperature extremes: A time series analysis in Varanasi, India. Sci. Total Environ., 665, 453–464, https://doi.org/10.1016/j.scitotenv.2019.02.074.

    • Search Google Scholar
    • Export Citation

  • Sippel, S., J. Zscheischler, M. D. Mahecha, R. Orth, M. Reichstein, M. Vogel, and S. I. Seneviratne, 2017: Refining multi-model projections of temperature extremes by evaluation against land–atmosphere coupling diagnostics. Earth Syst. Dyn., 8, 387–403, https://doi.org/10.5194/esd-8-387-2017.

    • Search Google Scholar
    • Export Citation

  • Stephens, G. L., and P. J. Webster, 1981: Clouds and climate: Sensitivity of simple systems. J. Atmos. Sci., 38, 235–247, https://doi.org/10.1175/1520-0469(1981)038<0235:CACSOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Swann, A. L. S., I. Y. Fung, and J. C. H. Chiang, 2012: Mid-latitude afforestation shifts general circulation and tropical precipitation. Proc. Natl. Acad. Sci. USA, 109, 712–716, https://doi.org/10.1073/pnas.1116706108.

    • Search Google Scholar
    • Export Citation

  • Tamarin-Brodsky, T., K. Hodges, B. J. Hoskins, and T. G. Shepherd, 2020: Changes in Northern Hemisphere temperature variability shaped by regional warming patterns. Nat. Geosci., 13, 414–421, https://doi.org/10.1038/s41561-020-0576-3.

    • Search Google Scholar
    • Export Citation

  • Teuling, A. J., and Coauthors, 2010: Contrasting response of European forest and grassland energy exchange to heatwaves. Nat. Geosci., 3, 722–727, https://doi.org/10.1038/ngeo950.

    • Search Google Scholar
    • Export Citation

  • Vargas Zeppetello, L. R., and D. S. Battisti, 2020: Projected increases in monthly midlatitude summertime temperature variance over land are driven by local thermodynamics. Geophys. Res. Lett., 47, e2020GL090197, https://doi.org/10.1029/2020GL090197.

    • Search Google Scholar
    • Export Citation

  • Vargas Zeppetello, L. R., D. S. Battisti, and M. B. Baker, 2019a: The origin of soil moisture evaporation “regimes”. J. Climate, 32, 6939–6960, https://doi.org/10.1175/JCLI-D-19-0209.1.

    • Search Google Scholar
    • Export Citation

  • Vargas Zeppetello, L. R., A. Donohoe, and D. S. Battisti, 2019b: Does surface temperature respond to or determine downwelling longwave radiation? Geophys. Res. Lett., 46, 2781–2789, https://doi.org/10.1029/2019GL082220.

    • Search Google Scholar
    • Export Citation

  • Vargas Zeppetello, L. R., D. S. Battisti, and M. B. Baker, 2020: A new look at the variance of summertime temperatures over land. J. Climate, 33, 5465–5477, https://doi.org/10.1175/JCLI-D-19-0887.1.

    • Search Google Scholar
    • Export Citation

  • Vogel, E., M. G. Donat, L. V. Alexander, M. Meinshausen, D. K. Ray, D. Karoly, N. Meinshausen, and K. Frieler, 2019: The effects of climate extremes on global agricultural yields. Environ. Res. Lett., 14, 054010, https://doi.org/10.1088/1748-9326/ab154b.

    • Search Google Scholar
    • Export Citation

  • von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Cambridge University Press, 484 pp., https://doi.org/10.1017/CBO9780511612336.

  • Wallace, J. M., Y. Zhang, and J. A. Renwick, 1995: Dynamic contribution to hemispheric mean temperature trends. Science, 270, 780–783, https://doi.org/10.1126/science.270.5237.780.

    • Search Google Scholar
    • Export Citation

  • Wehrli, K., B. P. Guillod, M. Hauser, M. Leclair, and S. I. Seneviratne, 2019: Identifying key driving processes of major recent heat waves. J. Geophys. Res. Atmos., 124, 11 746–11 765, https://doi.org/10.1029/2019JD030635.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 2016: “The stippling shows statistically significant grid points”: How research results are routinely overstated and overinterpreted, and what to do about it. Bull. Amer. Meteor. Soc., 97, 2263–2273, https://doi.org/10.1175/BAMS-D-15-00267.1.

    • Search Google Scholar
    • Export Citation

  • Zscheischler, J., R. Orth, and S. I. Seneviratne, 2015: A submonthly database for detecting changes in vegetation–atmosphere coupling. Geophys. Res. Lett., 42, 9816–9824, https://doi.org/10.1002/2015GL066563.

    • Search Google Scholar
    • Export Citation
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