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precipitation and atmospheric moisture content is related to the constraint imposed by the atmospheric radiative cooling due to the increased temperature and humidity, which limits the precipitation change through the global energy and water balances ( Allen and Ingram 2002 ; Pendergrass and Hartmann 2014 ). Regional differences of the precipitation response are much less clear, particularly over the tropics, where spatial and temporal shifts related to dynamic and thermodynamic responses to warming can
precipitation and atmospheric moisture content is related to the constraint imposed by the atmospheric radiative cooling due to the increased temperature and humidity, which limits the precipitation change through the global energy and water balances ( Allen and Ingram 2002 ; Pendergrass and Hartmann 2014 ). Regional differences of the precipitation response are much less clear, particularly over the tropics, where spatial and temporal shifts related to dynamic and thermodynamic responses to warming can
Earth System Modeling Framework (ESMF) regridding software ( https://www.ncl.ucar.edu/Applications/ESMF.shtml ) and the NetCDF Operators ( Zender 2019 ). Brightness temperature T b and precipitation (i.e., flux variables) are regridded using conservative mapping, and MCS location masks are regridded using nearest neighbor to preserve their tracked numbers. The regridding procedure essentially creates a reference MCS dataset that depicts MCS T b and precipitation characteristics at the respective
Earth System Modeling Framework (ESMF) regridding software ( https://www.ncl.ucar.edu/Applications/ESMF.shtml ) and the NetCDF Operators ( Zender 2019 ). Brightness temperature T b and precipitation (i.e., flux variables) are regridded using conservative mapping, and MCS location masks are regridded using nearest neighbor to preserve their tracked numbers. The regridding procedure essentially creates a reference MCS dataset that depicts MCS T b and precipitation characteristics at the respective
account in the original genesis index. Zhao and Held (2012) explored the relationship of TC activity with various environmental variables using one of the climate models from our study (P3) and found that the strongest relationship was with vertical velocity at 500 hPa. Furthermore, the same authors argued in Held and Zhao (2011) that the atmospheric vertical mass flux can be useful in understanding the reduction of TC hurricane activity in their idealized climate change experiments. However
account in the original genesis index. Zhao and Held (2012) explored the relationship of TC activity with various environmental variables using one of the climate models from our study (P3) and found that the strongest relationship was with vertical velocity at 500 hPa. Furthermore, the same authors argued in Held and Zhao (2011) that the atmospheric vertical mass flux can be useful in understanding the reduction of TC hurricane activity in their idealized climate change experiments. However
vapor mixing ratio, and all other variables have their usual meaning. We perform our analysis on model levels where p b is set to 920 hPa and p t is set to the model top. We describe the motivations for and implications of this choice in the appendix . The budget for column-integrated moist static energy is given by (2) ∂ h ^ ∂ t = F k + N L + N S − u ⋅ ∇ h ^ , where F k is the surface moist enthalpy flux, N L is the column longwave radiative flux convergence, and N S is the column
vapor mixing ratio, and all other variables have their usual meaning. We perform our analysis on model levels where p b is set to 920 hPa and p t is set to the model top. We describe the motivations for and implications of this choice in the appendix . The budget for column-integrated moist static energy is given by (2) ∂ h ^ ∂ t = F k + N L + N S − u ⋅ ∇ h ^ , where F k is the surface moist enthalpy flux, N L is the column longwave radiative flux convergence, and N S is the column
choose the RCP8.5 simulation to maximize the projected changes in the future and the potential differences between models. We analyze the following variables: total ET and its components (transpiration, soil evaporation, and canopy interception) and surface climate variables such as 2-m temperature and turbulent and radiative land–atmosphere fluxes. For vegetation data, we focus primarily on the leaf area index (LAI). Indeed, LAI is the primary vegetation-related variable considered in studies that
choose the RCP8.5 simulation to maximize the projected changes in the future and the potential differences between models. We analyze the following variables: total ET and its components (transpiration, soil evaporation, and canopy interception) and surface climate variables such as 2-m temperature and turbulent and radiative land–atmosphere fluxes. For vegetation data, we focus primarily on the leaf area index (LAI). Indeed, LAI is the primary vegetation-related variable considered in studies that
ME equation can then be written as where the square brackets represent mass-weighted vertical integrals from 1000 to 100 hPa, v is the horizontal wind, ω is the vertical pressure velocity, F s is total surface fluxes (including latent and sensible heat fluxes), and Q R is vertically integrated radiative heat fluxes (including longwave and shortwave heat fluxes). A brief validation of coherence between rainfall and ME anomalies associated with northward propagation of the MJO is first
ME equation can then be written as where the square brackets represent mass-weighted vertical integrals from 1000 to 100 hPa, v is the horizontal wind, ω is the vertical pressure velocity, F s is total surface fluxes (including latent and sensible heat fluxes), and Q R is vertically integrated radiative heat fluxes (including longwave and shortwave heat fluxes). A brief validation of coherence between rainfall and ME anomalies associated with northward propagation of the MJO is first
our analysis to the boreal summer months of June–September (JJAS). The following AM4.0 fields are used in this study: the horizontal winds u and υ , geopotential height Z , specific humidity q , precipitation P , dry static energy s , frozen moist static energy h , surface and top of the atmosphere shortwave (SW) and longwave (LW) radiative fluxes, and surface sensible H and latent heat fluxes E . In addition to daily data from AM4.0, two other datasets are used in this study. We make
our analysis to the boreal summer months of June–September (JJAS). The following AM4.0 fields are used in this study: the horizontal winds u and υ , geopotential height Z , specific humidity q , precipitation P , dry static energy s , frozen moist static energy h , surface and top of the atmosphere shortwave (SW) and longwave (LW) radiative fluxes, and surface sensible H and latent heat fluxes E . In addition to daily data from AM4.0, two other datasets are used in this study. We make
atmosphere are a nonunique function of cloud microphysical properties (drop number and liquid water path). Thus, constraining radiative effects of clouds is better done in conjunction with detailed observations of cloud microphysics than with just radiative fluxes. EXISTING PROCESS-ORIENTED DIAGNOSTIC EFFORTS. The MDTF PODs effort is inspired by, builds upon, and in many cases is complementary to prior and existing community efforts at model diagnosis. Such existing efforts that have influenced the MDTF
atmosphere are a nonunique function of cloud microphysical properties (drop number and liquid water path). Thus, constraining radiative effects of clouds is better done in conjunction with detailed observations of cloud microphysics than with just radiative fluxes. EXISTING PROCESS-ORIENTED DIAGNOSTIC EFFORTS. The MDTF PODs effort is inspired by, builds upon, and in many cases is complementary to prior and existing community efforts at model diagnosis. Such existing efforts that have influenced the MDTF
centered at the TC but excluding the inner 1000-km square area. The moisture–convection coupling within the TCs is analyzed using precipitation, precipitable water, and free-tropospheric (850–100 hPa) column relative humidity (CRH). Surface turbulent fluxes and surface and TOA radiative fluxes are employed to examine the surface enthalpy flux feedback and the cloud–radiation feedback processes. d. Composite on precipitation percentiles To provide further insights among models in their TC simulations
centered at the TC but excluding the inner 1000-km square area. The moisture–convection coupling within the TCs is analyzed using precipitation, precipitable water, and free-tropospheric (850–100 hPa) column relative humidity (CRH). Surface turbulent fluxes and surface and TOA radiative fluxes are employed to examine the surface enthalpy flux feedback and the cloud–radiation feedback processes. d. Composite on precipitation percentiles To provide further insights among models in their TC simulations
adept at qualitatively linking CWV values to the precipitation onset if the lateral entrainment is replaced with a prescribed environmental deep inflow: a constantly increasing mass flux with height (from the surface to the midtroposphere). This paradigm of entrainment is consistent with the updraft (or an ensemble of updrafts) interacting with an inflow of air that can include a coherent, organized component instead of merely incorporating peripheral environmental air through small
adept at qualitatively linking CWV values to the precipitation onset if the lateral entrainment is replaced with a prescribed environmental deep inflow: a constantly increasing mass flux with height (from the surface to the midtroposphere). This paradigm of entrainment is consistent with the updraft (or an ensemble of updrafts) interacting with an inflow of air that can include a coherent, organized component instead of merely incorporating peripheral environmental air through small
