Climate models show that soil moisture and its subseasonal fluctuations have important impacts on the surface latent heat flux, thus regulating surface temperature variations. Using correlations between monthly anomalies in net absorbed radiative fluxes, precipitation, 2-m air temperature, and soil moisture in the ERA-Interim reanalysis and the HadCM3 climate model, we develop a linear diagnostic model to quantify the major effects of land–atmosphere interactions on summertime surface temperature variability. The spatial patterns in 2-m air temperature and soil moisture variance from the diagnostic model are consistent with those from the products from which it was derived, although the diagnostic model generally underpredicts soil moisture variance. We use the diagnostic model to quantify the impact of soil moisture, shortwave radiation, and precipitation anomalies on temperature variance in wet and dry regions. Consistent with other studies, we find that fluctuations in soil moisture amplify temperature variance in dry regions through their impact on latent heat flux, whereas in wet regions temperature variability is muted because of high mean evapotranspiration rates afforded by plentiful surface soil moisture. We demonstrate how the diagnostic model can be used to identify sources of temperature variance bias in climate models.
The increasing frequency of very high summertime temperatures has motivated growing interest in the processes determining the probability distribution of surface temperature over land. Here, we show that on monthly time scales, temperature anomalies can be modeled as linear responses to fluctuations in shortwave radiation and precipitation. Our model contains only three adjustable parameters, and, surprisingly, these can be taken as constant across the globe, notwithstanding large spatial variability in topography, vegetation, and hydrological processes. Using observations of shortwave radiation and precipitation from 2000 to 2017, the model accurately reproduces the observed pattern of temperature variance throughout the Northern Hemisphere midlatitudes. In addition, the variance in latent heat flux estimated by the model agrees well with the few long-term records that are available in the central United States. As an application of the model, we investigate the changes in the variance of monthly averaged surface temperature that might be expected due to anthropogenic climate change. We find that a climatic warming of 4°C causes a 10% increase in temperature variance in parts of North America.
more » « less- NSF-PAR ID:
- 10156842
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 33
- Issue:
- 13
- ISSN:
- 0894-8755
- Page Range / eLocation ID:
- p. 5465-5477
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Most climate models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer pronounced warm and dry summer biases in the central United States (CUS), even in high-resolution simulations. We found that the cloud base definition in the cumulus parameterization was the dominant factor determining the spread of the biases among models and those defining cloud base at the lifting condensation level (LCL) performed the best. To identify the underlying mechanisms, we developed a physically based analytical bias model (ABM) to capture the key feedback processes of land–atmosphere coupling. The ABM has significant explanatory power, capturing 80% variance of temperature and precipitation biases among all models. Our ABM analysis via counterfactual experiments indicated that the biases are attributed mostly by surface downwelling longwave radiation errors and second by surface net shortwave radiation errors, with the former 2–5 times larger. The effective radiative forcing from these two errors as weighted by their relative contributions induces runaway temperature and precipitation feedbacks, which collaborate to cause CUS summer warm and dry biases. The LCL cumulus reduces the biases through two key mechanisms: it produces more clouds and less precipitable water, which reduce radiative energy input for both surface heating and evapotranspiration to cause a cooler and wetter soil; it produces more rainfall and wetter soil conditions, which suppress the positive evapotranspiration–precipitation feedback to damp the warm and dry bias coupling. Most models using non-LCL schemes underestimate both precipitation and cloud amounts, which amplify the positive feedback to cause significant biases.
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This dataset contains monthly average output files from the iCAM6 simulations used in the manuscript "Enhancing understanding of the hydrological cycle via pairing of process-oriented and isotope ratio tracers," in review at the Journal of Advances in Modeling Earth Systems. A file corresponding to each of the tagged and isotopic variables used in this manuscript is included. Files are at 0.9° latitude x 1.25° longitude, and are in NetCDF format. Data from two simulations are included: 1) a simulation where the atmospheric model was "nudged" to ERA5 wind and surface pressure fields, by adding an additional tendency (see section 3.1 of associated manuscript), and 2) a simulation where the atmospheric state was allowed to freely evolve, using only boundary conditions imposed at the surface and top of atmosphere. Specific information about each of the variables provided is located in the "usage notes" section below. Associated article abstract: The hydrologic cycle couples the Earth's energy and carbon budgets through evaporation, moisture transport, and precipitation. Despite a wealth of observations and models, fundamental limitations remain in our capacity to deduce even the most basic properties of the hydrological cycle, including the spatial pattern of the residence time (RT) of water in the atmosphere and the mean distance traveled from evaporation sources to precipitation sinks. Meanwhile, geochemical tracers such as stable water isotope ratios provide a tool to probe hydrological processes, yet their interpretation remains equivocal despite several decades of use. As a result, there is a need for new mechanistic tools that link variations in water isotope ratios to underlying hydrological processes. Here we present a new suite of “process-oriented tags,” which we use to explicitly trace hydrological processes within the isotopically enabled Community Atmosphere Model, version 6 (iCAM6). Using these tags, we test the hypotheses that precipitation isotope ratios respond to parcel rainout, variations in atmospheric RT, and preserve information regarding meteorological conditions during evaporation. We present results for a historical simulation from 1980 to 2004, forced with winds from the ERA5 reanalysis. We find strong evidence that precipitation isotope ratios record information about atmospheric rainout and meteorological conditions during evaporation, but little evidence that precipitation isotope ratios vary with water vapor RT. These new tracer methods will enable more robust linkages between observations of isotope ratios in the modern hydrologic cycle or proxies of past terrestrial environments and the environmental processes underlying these observations. Details about the simulation setup can be found in section 3 of the associated open-source manuscript, "Enhancing understanding of the hydrological cycle via pairing of process‐oriented and isotope ratio tracers." In brief, we conducted two simulations of the atmosphere from 1980-2004 using the isotope-enabled version of the Community Atmosphere Model 6 (iCAM6) at 0.9x1.25° horizontal resolution, and with 30 vertical hybrid layers spanning from the surface to ~3 hPa. In the first simulation, wind and surface pressure fields were "nudged" toward the ERA5 reanalysis dataset by adding a nudging tendency, preventing the model from diverging from observed/reanalysis wind fields. In the second simulation, no additional nudging tendency was included, and the model was allowed to evolve 'freely' with only boundary conditions provided at the top (e.g., incoming solar radiation) and bottom (e.g., observed sea surface temperatures) of the model. In addition to the isotopic variables, our simulation included a suite of 'process-oriented tracers,' which we describe in section 2 of the manuscript. These variables are meant to track a property of water associated with evaporation, condensation, or atmospheric transport. Metadata are provided about each of the files below; moreover, since the attached files are NetCDF data - this information is also provided with the data files. NetCDF metadata can be accessed using standard tools (e.g., ncdump). Each file has 4 variables: the tagged quantity, and the associated coordinate variables (time, latitude, longitude). The latter three are identical across all files, only the tagged quantity changes. Twelve files are provided for the nudged simulation, and an additional three are provided for the free simulations: Nudged simulation files iCAM6_nudged_1980-2004_mon_RHevap: Mass-weighted mean evaporation source property: RH (%) with respect to surface temperature. iCAM6_nudged_1980-2004_mon_Tevap: Mass-weighted mean evaporation source property: surface temperature in Kelvin iCAM6_nudged_1980-2004_mon_Tcond: Mass-weighted mean condensation property: temperature (K) iCAM6_nudged_1980-2004_mon_columnQ: Total (vertically integrated) precipitable water (kg/m2). Not a tagged quantity, but necessary to calculate depletion times in section 4.3 (e.g., Fig. 11 and 12). iCAM6_nudged_1980-2004_mon_d18O: Precipitation d18O (‰ VSMOW) iCAM6_nudged_1980-2004_mon_d18Oevap_0: Mass-weighted mean evaporation source property - d18O of the evaporative flux (e.g., the 'initial' isotope ratio prior to condensation), (‰ VSMOW) iCAM6_nudged_1980-2004_mon_dxs: Precipitation deuterium excess (‰ VSMOW) - note that precipitation d2H can be calculated from this file and the precipitation d18O as d2H = d-excess - 8*d18O. iCAM6_nudged_1980-2004_mon_dexevap_0: Mass-weighted mean evaporation source property - deuterium excess of the evaporative flux iCAM6_nudged_1980-2004_mon_lnf: Integrated property - ln(f) calculated from the constant-fractionation d18O tracer (see section 3.2). iCAM6_nudged_1980-2004_mon_precip: Total precipitation rate in m/s. Note there is an error in the metadata in this file - it is total precipitation, not just convective precipitation. iCAM6_nudged_1980-2004_mon_residencetime: Mean atmospheric water residence time (in days). iCAM6_nudged_1980-2004_mon_transportdistance: Mean atmospheric water transport distance (in km). Free simulation files iCAM6_free_1980-2004_mon_d18O: Precipitation d18O (‰ VSMOW) iCAM6_free_1980-2004_mon_dxs: Precipitation deuterium excess (‰ VSMOW) - note that precipitation d2H can be calculated from this file and the precipitation d18O as d2H = d-excess - 8*d18O. iCAM6_free_1980-2004_mon_precip: Total precipitation rate in m/s. Note there is an error in the metadata in this file - it is total precipitation, not just convective precipitation.more » « less
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null (Ed.)Abstract Multi-time-scale variabilities of the Indian Ocean (IO) temperature over 0–700 m are revisited from the perspective of vertical structure. Analysis of historical data for 1955–2018 identifies two dominant types of vertical structures that account for respectively 70.5% and 21.2% of the total variance on interannual-to-interdecadal time scales with the linear trend and seasonal cycle removed. The leading type manifests as vertically coherent warming/cooling with the maximal amplitude at ~100 m and exhibits evident interdecadal variations. The second type shows a vertical dipole structure between the surface (0–60 m) and subsurface (60–400 m) layers and interannual-to-decadal fluctuations. Ocean model experiments were performed to gain insights into underlying processes. The vertically coherent, basinwide warming/cooling of the IO on an interdecadal time scale is caused by changes of the Indonesian Throughflow (ITF) controlled by Pacific climate and anomalous surface heat fluxes partly originating from external forcing. Enhanced changes in the subtropical southern IO arise from positive air–sea feedback among sea surface temperature, winds, turbulent heat flux, cloud cover, and shortwave radiation. Regarding dipole-type variability, the basinwide surface warming is induced by surface heat flux forcing, and the subsurface cooling occurs only in the eastern IO. The cooling in the southeast IO is generated by the weakened ITF, whereas that in the northeast IO is caused by equatorial easterly winds through upwelling oceanic waves. Both El Niño–Southern Oscillation (ENSO) and IO dipole (IOD) events are favorable for the generation of such vertical dipole anomalies.more » « less
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Abstract Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.
