Search Results
and land models are active while sea surface temperature (SST) and sea ice cover are prescribed based on observations. We use the SST and sea ice cover from the ERA-Interim reanalysis ( Dee et al. 2011 ). The 6-hourly ERA-Interim data are averaged to daily for used as model input. The land model is the Community Land Model version 4 ( Lawrence et al. 2011 ) with prescribed, static land cover types and vegetation properties (e.g., leaf area index) roughly representing the conditions for year 2000
and land models are active while sea surface temperature (SST) and sea ice cover are prescribed based on observations. We use the SST and sea ice cover from the ERA-Interim reanalysis ( Dee et al. 2011 ). The 6-hourly ERA-Interim data are averaged to daily for used as model input. The land model is the Community Land Model version 4 ( Lawrence et al. 2011 ) with prescribed, static land cover types and vegetation properties (e.g., leaf area index) roughly representing the conditions for year 2000
tropical cyclone activity under future warming scenarios using a high-resolution climate model . Climatic Change , 146 , 547 – 560 , https://doi.org/10.1007/s10584-016-1750-x . 10.1007/s10584-016-1750-x Bao , Q. , and Coauthors , 2013 : The Flexible Global Ocean–Atmosphere–Land System model, spectral version 2: FGOALS-s2 . Adv. Atmos. Sci. , 30 , 561 – 576 , https://doi.org/10.1007/s00376-012-2113-9 . 10.1007/s00376-012-2113-9 Bell , G. D. , and Coauthors , 2000 : Climate assessment for
tropical cyclone activity under future warming scenarios using a high-resolution climate model . Climatic Change , 146 , 547 – 560 , https://doi.org/10.1007/s10584-016-1750-x . 10.1007/s10584-016-1750-x Bao , Q. , and Coauthors , 2013 : The Flexible Global Ocean–Atmosphere–Land System model, spectral version 2: FGOALS-s2 . Adv. Atmos. Sci. , 30 , 561 – 576 , https://doi.org/10.1007/s00376-012-2113-9 . 10.1007/s00376-012-2113-9 Bell , G. D. , and Coauthors , 2000 : Climate assessment for
and Schumacher 2015 , and references therein). Several studies empirically approximate the relationship using an exponential function with a “pick-up” precipitable water value at which precipitation starts to increase rapidly with precipitable water ( Igel et al. 2017 ; Sahany et al. 2014 ). Conceptually this relationship provides a measure of the effectiveness of environmental air in diluting rising plumes in moist convection ( Peters et al. 2009 ; Holloway and Neelin 2009 , etc.) and the
and Schumacher 2015 , and references therein). Several studies empirically approximate the relationship using an exponential function with a “pick-up” precipitable water value at which precipitation starts to increase rapidly with precipitable water ( Igel et al. 2017 ; Sahany et al. 2014 ). Conceptually this relationship provides a measure of the effectiveness of environmental air in diluting rising plumes in moist convection ( Peters et al. 2009 ; Holloway and Neelin 2009 , etc.) and the
1. Introduction The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) benchmarking experiments by Best et al. (2015) showed that some of the world’s most sophisticated operational land models (CABLE, CH-TESSEL, COLA-SSiB, ISBA-SURFEX, JULES, Mosaic, Noah, ORCHIDEE) were outperformed in their ability to simulate short-term surface energy fluxes by simple regressions. Specifically, the PLUMBER experiments used piecewise
1. Introduction The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) benchmarking experiments by Best et al. (2015) showed that some of the world’s most sophisticated operational land models (CABLE, CH-TESSEL, COLA-SSiB, ISBA-SURFEX, JULES, Mosaic, Noah, ORCHIDEE) were outperformed in their ability to simulate short-term surface energy fluxes by simple regressions. Specifically, the PLUMBER experiments used piecewise
1. Introduction a. Background and motivation Land surface models simulate distinct diurnal cycles of turbulent heat fluxes, but they also show systematic deviations from observations, which were reported in early ( Henderson-Sellers et al. 1995 ; Chen et al. 1997 ) and more recent model intercomparison studies ( Holtslag et al. 2013 ; Best et al. 2015 ). Best et al. (2015) used observational meteorological forcing to drive and evaluate state-of-the-art models at 20 different flux towers. A
1. Introduction a. Background and motivation Land surface models simulate distinct diurnal cycles of turbulent heat fluxes, but they also show systematic deviations from observations, which were reported in early ( Henderson-Sellers et al. 1995 ; Chen et al. 1997 ) and more recent model intercomparison studies ( Holtslag et al. 2013 ; Best et al. 2015 ). Best et al. (2015) used observational meteorological forcing to drive and evaluate state-of-the-art models at 20 different flux towers. A
Coupled Model Intercomparison Project (CMIP5). Several studies have addressed soil moisture–atmosphere coupling in CMIP5 models ( Williams et al. 2012 ; Taylor et al. 2012 ; Levine et al. 2016 ; Herrera-Estrada and Sheffield 2017 ). Such studies generally endeavor to provide an observational benchmark of land–atmosphere coupling, typically quantified through a specific metric calculated from remote sensing or in situ data, against which models can be evaluated. Other studies have also used CMIP5
Coupled Model Intercomparison Project (CMIP5). Several studies have addressed soil moisture–atmosphere coupling in CMIP5 models ( Williams et al. 2012 ; Taylor et al. 2012 ; Levine et al. 2016 ; Herrera-Estrada and Sheffield 2017 ). Such studies generally endeavor to provide an observational benchmark of land–atmosphere coupling, typically quantified through a specific metric calculated from remote sensing or in situ data, against which models can be evaluated. Other studies have also used CMIP5
-scale turbulence. The convective transition statistics thus have potential to contribute constraints for reworking some of the basic assumptions about entrainment as currently implemented in cumulus parameterization schemes. The convective transition statistics over the tropical oceans have received much attention in the preceding decade, with only a few recent investigations of these statistics over tropical land. Ahmed and Schumacher (2017) , using satellite and reanalysis data, showed that for land regions
-scale turbulence. The convective transition statistics thus have potential to contribute constraints for reworking some of the basic assumptions about entrainment as currently implemented in cumulus parameterization schemes. The convective transition statistics over the tropical oceans have received much attention in the preceding decade, with only a few recent investigations of these statistics over tropical land. Ahmed and Schumacher (2017) , using satellite and reanalysis data, showed that for land regions
in the future. Atmospheric and land–atmosphere flux variables were typically available for more than 40 models. Models used are listed in Table S1 in the online supplemental material. To analyze the relationship between ET partitioning and other aspects of model simulations, such as vegetation or surface climate, we compute cross-model (Pearson) correlations. That is, for a given pair of variables, we compute the correlation across models between long-term means for these variables, on a pixel
in the future. Atmospheric and land–atmosphere flux variables were typically available for more than 40 models. Models used are listed in Table S1 in the online supplemental material. To analyze the relationship between ET partitioning and other aspects of model simulations, such as vegetation or surface climate, we compute cross-model (Pearson) correlations. That is, for a given pair of variables, we compute the correlation across models between long-term means for these variables, on a pixel
2007 ), because the CMIP5 data archive does not include 6-hourly PWV data. We use ERAI to test if this replacement is reasonable. For the Northern Hemisphere, for the latitudes between 20° and 65°N, we calculate the spatial correlation of 6-hourly snapshots of PWV and Q850 for a set of 100 randomly selected dates. For these cases, the correlation between PWV and Q850 has an average value of 0.92 and is never less than 0.9. If the land is excluded, the average correlation value is 0.94. We also
2007 ), because the CMIP5 data archive does not include 6-hourly PWV data. We use ERAI to test if this replacement is reasonable. For the Northern Hemisphere, for the latitudes between 20° and 65°N, we calculate the spatial correlation of 6-hourly snapshots of PWV and Q850 for a set of 100 randomly selected dates. For these cases, the correlation between PWV and Q850 has an average value of 0.92 and is never less than 0.9. If the land is excluded, the average correlation value is 0.94. We also
values from spatial interpolation (e.g., around the Andes and New Guinea), data in the 2.5° neighborhood of land pixels are excluded for some of the presented statistics. Note that the CWV datasets often do not record a CWV value in the presence of precipitation, and thus gap filling is required to reconstruct missing data (see section S1 in the supplementary material). For algorithm choices used for the TMIv7.1 data, the probability of missing CWV depends primarily on P , with the probability
values from spatial interpolation (e.g., around the Andes and New Guinea), data in the 2.5° neighborhood of land pixels are excluded for some of the presented statistics. Note that the CWV datasets often do not record a CWV value in the presence of precipitation, and thus gap filling is required to reconstruct missing data (see section S1 in the supplementary material). For algorithm choices used for the TMIv7.1 data, the probability of missing CWV depends primarily on P , with the probability
