Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping

When applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diuWhen applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.rnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values

NASA Global Daily Downscaled Projections, CMIP6

We describe the latest version of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). The archive contains downscaled historical and future projections for 1950-2100 based on output from Phase 6 of the Climate Model Intercomparison Project (CMIP6). The downscaled products were produced using a daily variant of the monthly bias correction/spatial disaggregation (BCSD) method and are at 1/4-degree horizontal resolution. Currently, eight variables from five CMIP6 experiments (historical, SSP126, SSP245, SSP370, and SSP585) are provided as procurable from thirty-five global climate models

Making Climate Data Relevant to Decision Making: The important details of Spatial and Temporal Downscaling

This paper examines potential regional-scale impacts of climate change on sustainability of irrigated agriculture, focusing on the western San Joaquin Valley in California. We consider potential changes in irrigation water demand and supply, and quantify impacts on the hydrologic system, soil and groundwater salinity with associated crop yield reductions. Our analysis is based on archived output from General Circulation Model (GCM) climate projections through 2100, which were downscaled to the 1,400 km2 study area. We account for uncertainty in GCM climate projections by considering two different GCM\u27s, each using three greenhouse gas emission scenarios. Significant uncertainty in projected precipitation creates large uncertainty in surface water supply, ranging from a decrease of 26% to an increase of 14% in 2080-2099. Changes in projected irrigation water demand ranged from a decrease of 13% to an increase of 3% at the end of the 21st century. Greatest demand reductions were computed for the dry and warm scenarios, because of increased land fallowing with corresponding decreased total crop water requirements. A decrease in seasonal crop ET by climate warming, despite an increase in evaporative demand, was attributed to faster crop development with increasing temperatures. Simulations of hydrologic response to climate-induced changes suggest that the salt-affected area will be slightly expanded. However, irrespective of climate change, salinity is expected to increase in downslope areas, thereby limiting crop production to mostly upslope areas of the simulation domain. Results show that increasing irrigation efficiency may be effective in controlling salinization, by reducing groundwater recharge and improving soil drainage, and in mitigating climate warming effects, by reducing the need for groundwater pumping to satisfy crop water requirements

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km by 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future

Soil moisture droughts under the retrospective and projected climate in India

Changes in precipitation, air temperature, and model simulated soil moisture were examined for the observed (1950-2008) and projected future (2010-2099) climate for the sowing period of Kharif and Rabi [(KHARIF_SOW (May-July) and RABI_SOW (October-December)], and the entire Kharif and Rabi [(KHARIF (May-October) and RABI (October-April)] crop-growing periods in India. During the KHARIF_SOW and KHARIF periods, precipitation declined significantly in the Gangetic Plain, which in turn resulted in declines in soil moisture. Statistically significant warming trends were noticed as all-India averaged air temperature increased by 0.40, 0.90, and 0.70 °C in the KHARIF, RABI_SOW, and RABI periods, respectively during the period of 1950-2008. Frequency and areal extent of soil moisture based droughts increased substantially during the latter half (1980-2008) of the observed period. Under the projected future climate (2010-2099), precipitation, air temperature, and soil moisture are projected to increase in all the four crop-growing seasons. In the projected future climate, all-India ensemble mean precipitation, air temperature, and soil moisture are projected to increase up to 39% (RABI_SOW period), 2.3 °C, and 5.3 %, respectively in the crop-growing periods. While projected changes in air temperature are robust across India, robust increases in precipitation and soil moisture are projected to occur in the End (2070-2099) term climate. Frequency and areal extents of soil moisture based severe, extreme and exceptional droughts are projected to increase in the Near (2010-2039) and Mid (2040-2069) term climate in the majority of crop growing seasons in India. However, frequency and areal extent of droughts during the crop growing period are projected to decline in the End term climate in the entire crop growing period due to projected increases in the monsoon season precipitation.by Vimal Mishra, Reepal Shah and Bridget Thrashe

Dataset of monthly downscaled future vapor pressure projections for the conterminous USA for RCP 4.5 and RCP 8.5 compatible with NEX-DCP30

Models that simulate ecosystems at local to regional scales require relatively fine resolution climate data. Many methods exist that downscale the native resolution output from global climate models (GCM) to finer resolutions. NASA NEX-DCP30 is a statistically downscaled 30 arcsecond resolution climate dataset widely used for climate change impact studies in the conterminous USA (CONUS), but it did not include vapor pressure data which is essential for many types of models. We downscaled vapor pressure data from 28 global climate models included in the Coupled Model Intercomparison Project Phase 5 (CMIP5) to 30 arcsecond resolution for CONUS to augment the NEX-DCP30 dataset. Monthly vapor pressure values were calculated from raw GCM output for the conterminous USA from 1950 to 2100, representing RCP4.5 and RCP8.5 climate change scenarios. Vapor pressure data were then downscaled from the GCM's native spatial resolutions to 30 arcsecond using the Bias Correction-Spatial Disaggregation (BCSD) statistical downscaling method, which had been used to create the original NEX-DCP30 dataset. PRISM LT71m gridded climate data for 1970-1999 served as the reference data. The newly created downscaled vapor pressure dataset may be used in conjunction with the existing NEX-DCP30 data as input for vegetation, fire, drought, or earth system models. The data is available at the Forest Service Research Data Archive

Climate change impacts on an alpine watershed in Chile: Do new model projections change the story?

Due to global warming the climate of central Chile is expected to experience dramatic changes in the 21st century including declining precipitation, earlier streamflow peaks, and a greater proportion of precipitation falling as rain. We used 12-member ensembles of General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5) to evaluate climate-attributed changes in the hydrology of the Mataquito river basin in central Chile, South America. Simulations using the Variable Infiltration Capacity (VIC) hydrology model indicate that a drier and warmer future will shift the location of snow line to higher elevations and reduce the number of days with precipitation falling as snow. Extreme precipitation and streamflow events are expected to become more frequent. Conversely, low flow conditions will intensify during the warm months. The changes in the mean of hydrologic states and fluxes by the end of the 21st century are statistically robust, whereas changes in the variance are not found to be statistically significant. Results of the ensembles for CMIP3 and CMIP5 are generally indistinguishable regarding projected impacts on hydrology

Hot summers in the Bighorn Basin during the early Paleogene

During the early Paleogene, climate in continental interiors is thought to have been warmer and more equable than today, but estimates of seasonal temperature variations during this period are limited. Global and regional climate models of the Paleogene predict cooler temperatures for continental interiors than are implied by proxy data and predict a seasonal range of temperature that is similar to today. Here, we present a record of summer temperatures derived from carbonate clumped isotope thermometry of paleosol carbonates from Paleogene deposits in the Bighorn Basin, Wyoming (United States). Our summer temperature estimates are ∼18 °C greater than mean annual temperature estimated from analysis of fossil leaves. When coupled, these two records yield a seasonal range of temperature similar to that in the region today, with winter temperatures that are near freezing. These data are consistent with our high-resolution climate model output for the Early Eocene in the Bighorn Basin. We suggest that temperatures in continental interiors during the early Paleogene greenhouse were warmer in all seasons, but not more equable than today. If generally true, this removes one of the long-standing paradoxes in our understanding of terrestrial climate dynamics under greenhouse conditions