Climate Analytics Group

Palo Alto, CA, United States

Climate Analytics Group

Palo Alto, CA, United States
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Demaria E.M.C.,University of Santiago de Chile | Maurer E.P.,Santa Clara University | Thrasher B.,Climate Analytics Group | Thrasher B.,Climate Central Inc. | And 2 more authors.
Journal of Hydrology | Year: 2013

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. © 2013 Elsevier B.V.


Maurer E.P.,Santa Clara University | Brekke L.,Bureau of Reclamation | Pruitt T.,Bureau of Reclamation | Thrasher B.,Climate Analytics Group | And 6 more authors.
Bulletin of the American Meteorological Society | Year: 2014

There has been considerable effort in developing shared tools to enable statistical downscaling by impacts modelers and decision makers, some aimed at generating meteorological projections at a point and others formulated to produce finescale gridded regional data. The archive has satisfied an initial incentive in its development of bridging the gap between the climate science community and the planning community, ultimately bringing pertinent climate change information to bear on locally focused impacts and adaptation efforts. The archive has also been leveraged by others catering to the specific interests and the needs of other communities. The archive data from the ClimateWizard has subsequently been used to develop a US-wide index to assess the vulnerability of plant and animal species to climate change.


Thrasher B.,Climate Analytics Group | Thrasher B.,Climate Central Inc. | Maurer E.P.,Santa Clara University | McKellar C.,San Jose State University | Duffy P.B.,Lawrence Livermore National Laboratory
Hydrology and Earth System Sciences | Year: 2012

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 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. © Author(s) 2012.


Pierce D.W.,University of California at San Diego | Cayan D.R.,University of California at San Diego | Cayan D.R.,U.S. Geological Survey | Thrasher B.L.,Climate Analytics Group
Journal of Hydrometeorology | Year: 2014

A new technique for statistically downscaling climate model simulations of daily temperature and precipitation is introduced and demonstrated over the western United States. The localized constructed analogs (LOCA)method produces downscaled estimates suitable for hydrological simulations using amultiscale spatial matching scheme to pick appropriate analog days fromobservations. First, a pool of candidate observed analog days is chosen by matching the model field to be downscaled to observed days over the region that is positively correlated with the point being downscaled, which leads to a natural independence of the downscaling results to the extent of the domain being downscaled. Then, the one candidate analog day that best matches in the local area around the grid cell being downscaled is the single analog day used there. Most grid cells are downscaled using only the single locally selected analog day, but locationswhose neighboring cells identify a different analog day use a weighted combination of the center and adjacent analog days to reduce edge discontinuities. By contrast, existing constructed analog methods typically use a weighted average of the same 30 analog days for the entire domain. By greatly reducing this averaging, LOCA produces better estimates of extreme days, constructs a more realistic depiction of the spatial coherence of the downscaled field, and reduces the problem of producing too many light-precipitation days. The LOCA method is more computationally expensive than existing constructed analog techniques, but it is still practical for downscaling numerous climate model simulations with limited computational resources. © 2014 American Meteorological Society.


Mishra V.,Indian Institute of Technology Gandhinagar | Shah R.,Indian Institute of Technology Gandhinagar | Thrasher B.,Climate Analytics Group
Journal of Hydrometeorology | Year: 2014

Changes in precipitation, air temperature, and model-simulated soil moisture were examined for the observed (1950-2008) and projected (2010-99) climate for the sowing period ofKharif and Rabi [KHARIF_SOW (May-July) andRABI_SOW(October-December)] and the entire Kharif andRabi [KHARIF (May-October) andRABI (October-April)] crop-growing periods in India. During theKHARIF_SOWandKHARIF 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 1950-2008. Frequency and areal extent of soilmoisture-based droughts increased substantially during the latter half (1980-2008) of the observed period. Under the projected climate (2010-99), precipitation, air temperature, and soil moisture are projected to increase in all four crop-growing seasons. In the projected climate, all-India ensemble mean precipitation, air temperature, and soil moisture are projected to increase up to 39%(RABI_SOW period), 2.38C, 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 endterm (2070-99) climate. Frequency and areal extents of soil moisture-based severe, extreme, and exceptional droughts are projected to increase in the near- (2010-39) and midterm (2040-69) 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 because of projected increases in the monsoon season precipitation. © 2014 American Meteorological Society.

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