CHG - Research

Climate Change and Adaptation

Now that there is consensus on anthropogenic long term global warming, the research focus must turn to predicting regional and local effects of climate change. The CHG engages in research activities aimed at predicting the effects of climate change on specific food producing regions and localities throughout the developing world. Predictive science carried out at CHG is used by researchers and technologists to develop effective climate change adaptation strategies, and by policy makers to allocate climate change adaptation resources.
The Centennial Trends Greater Horn of Africa precipitation dataset Contributors: Chris Funk, Marty Landsfeld, Pete Peterson, Laura Harrison

East Africa is a drought prone, food and water insecure region with a highly variable climate. This complexity makes rainfall estimation challenging, and this challenge is compounded by low rain gauge densities and inhomogeneous monitoring networks. The dearth of observations is particularly problematic over the past decade, since the number of records in globally accessible archives has fallen precipitously. This lack of data coincides with an increasing scientific and humanitarian need to place recent seasonal and multi-annual East African precipitation extremes in a deep historic context. To serve this need, scientists from the UC Santa Barbara Climate Hazards Group and Florida State University have pooled their station archives and expertise to produce a high quality gridded 'Centennial Trends' precipitation dataset. Additional observations have been acquired from the national meteorological agencies and augmented with data provided by other universities. Extensive quality control of the data was carried out and seasonal anomalies interpolated using kriging. This paper documents the CenTrends methodology and data.

Annual Drought in California: Association with Monthly Precipitation and Climate Phases Principal Investigator: Shraddhanand Shukla

Annual precipitation in California is more variable than in any other state and is highly influenced by precipitation in winter months. A primary question among stakeholders is whether low precipitation in certain months is a harbinger of annual drought in California. Historical precipitation data from 1895 to 2013 are investigated to identify leading monthly indicators of annual drought in each of the seven climate divisions (CDs) as well as statewide. For this study, drought conditions are defined as monthly/annual (October–September) precipitation below the 20th/30th percentile, and a leading indicator is defined as a monthly drought preceding or during an annual drought that has the strongest association (i.e., joint probability of occurrence) with a statewide annual drought. Monthly precipitation variability and contributions to annual precipitation, along with joint probabilities of drought among the winter months, are first analyzed. Then the probabilities of annual drought and the variability in leading indicators are analyzed according to different climate phases and CDs. This study identified December within a water year as being the leading indicator that is most frequently associated with annual drought statewide (56%) and in most of the CDs (the highest was CD2 at 65%). Associated with its leading-indicator status, December drought was most frequently associated with drought in other winter months (joint probability > 30%). Results from this study can help stakeholders to understand and assess the likelihood of annual drought events given monthly precipitation preceding or early in the water year.

Famine Early Warning System Network (FEWS NET)

Scientists at the CHG develop a wide range of computer based models to predict food insecurity and famine conditions months ahead of time as part of USAID's Famine Early Warning Systems Network. These predictions help policy makers and aid agencies to prepare for food scarcity and allocate resources accordingly.
The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes Contributors: Chris Funk, Pete Peterson, Marty Landsfeld, Diego Pedreros, Shrad Shukla, Greg Husak, Laura Harrison, Andy Hoell, Joel Michaelsen

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to 'smart' interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.