Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.     

Long-term variability in the date of monsoon onset over western India

The date of onset of the southwest monsoon in western India is critical for farmers as it influences the timing of crop plantation and the duration of the summer rainy season. Identifying long-term variability in the date of monsoon onset is difficult, however, as onset dates derived from the reanalysis of instrumental rainfall data are only available for the region from 1879. This study uses documentary evidence and newly uncovered instrumental data to reconstruct annual monsoon onset dates for western India for the period 1781–1878, extending the existing record by 97 years. The mean date of monsoon onset over the Mumbai (Bombay) area during the reconstruction period was 10 June with a standard deviation of 6.9 days. This is similar to the mean and standard deviation of the date of monsoon onset derived from instrumental data for the twentieth century. The earliest identified onset date was 23 May (in 1802 and 1839) and the latest 22 June (in 1825). The longer-term perspective provided by this study suggests that the climatic regime that governs monsoon advance over western India did not change substantially from 1781 to 1955. Monsoon onset over Mumbai has occurred at a generally later date since this time. Our results indicate that this change is unprecedented during the last 230 years. Following a discussion of the results, the nature of the relationship between the date of monsoon onset and the El Niño-Southern Oscillation is discussed. This relationship is shown to have been stable since 1781.     

Quantifying global climate feedbacks, responses and forcing under abrupt and gradual CO2 forcing

Experiments with abrupt CO₂ forcing allow the diagnosis of the response of global mean temperature and precipitation in terms of fast temperature independent adjustments and slow, linear temperature-dependent feedbacks. Here we compare responses, feedbacks and forcings in experiments performed as part of version 5 of the coupled model inter-comparison project (CMIP5). The experiments facilitate, for the first time, a comparison of fully coupled atmosphere-ocean general circulation models (GCM’s) under both linearly increasing and abrupt radiative forcing. In the case of a 1 % per year compounded increase in CO₂ concentration, we find that the non-linear evolution of surface air temperature in time, when combined with the linear evolution of the radiative balance at the top of the atmosphere, results in a feedback parameter and effective climate sensitivity having an offset compared to values computed from abrupt 4× CO₂ forcing experiments. The linear evolution of the radiative balance at the top of the atmosphere also contributes to an offset between the global mean precipitation response predicted in the 1 % experiment using linear theory and that diagnosed from the experiments themselves, and a potential error between the adjusted radiative forcing and that produced using a standard linear formula. The non-linear evolution of temperature and precipitation responses are also evident in the RCP8.5 scenario and have implications for understanding, quantifying and emulating the global response of the CMIP5 climate GCMs.     

The climate-population nexus in the East African Horn: Emerging degradation trends in rangeland and pastoral livelihood zones

Increasing climate variability and extreme weather conditions along with declining trends in both rainfall and temperature represent major risk factors affecting agricultural production and food security in many regions of the world. The rangelands of Ethiopia, Kenya, and Somalia in the East African Horn remain one of the world's most food insecure regions, yet have substantially increasing human populations predominantly dependent on pastoralist and agro-pastoralist livelihoods. We identify regions where substantial rainfall decrease between two periods interrupted by the 1998 El Nino event (1981–2012) in the East African Horn is coupled with human population density increases. Vegetation in this region is characterized by a variable mosaic of land covers, generally dominated by grasslands necessary for agro-pastoralism, interspersed by woody vegetation. Recent assessments indicate that vegetation degradation is occurring, adversely impacting fragile ecosystems and human livelihoods. Using AVHRR and MODIS vegetation products from 1981 to 2012, we observe changes in vegetation patterns and productivity over the last decade across the East African Horn. We observe vegetation browning trends in areas experiencing reduced main-growing season precipitation; these areas are also concurrently experiencing increasing population pressures. We also found that the drying precipitation patterns only partially statistically explain the vegetation browning trends, indicating that other factors such as population pressures and land use changes might be responsible for the observed declining vegetation condition. Furthermore, we show that the general vegetation browning trends persist even during years with normal rainfall conditions such as 2012, pointing to potential long-term degradation of rangelands on which approximately 10 million people depend. These findings may have implications for current and future regional food security monitoring and forecasting as well as for mitigation and adaptation strategies in a region where population is expected to continue increasing against a backdrop of drying climate trends and increased climatic variability.     

The household production function approach to valuing climate: the case of Japan

According to household production function theory households combine marketed goods and nonmarket environmental goods to produce service flows of direct value to the household. This readily explains why, as an input to household production activities, households might have preferences over the climate. Using techniques more frequently employed to account for differences in the demographic composition of households we use household production function theory to estimate climate equivalence scales using household expenditure data drawn from 51 Japanese cities over the period 2000–2009. Our results indicate that warmer temperatures result in a small but statistically highly significant reduction in the cost of living. Combining these estimates with climate change scenarios associated with the IPCC A2, A1B, and B1 emissions scenarios other things being equal points to a slight reduction in Japanese households’ cost of living.     

The challenge to detect and attribute effects of climate change on human and natural systems

Future scenarios of the energy system under greenhouse gas emission constraints depict dramatic growth in a range of energy technologies. Technological growth dynamics observed historically provide a useful comparator for these future trajectories. We find that historical time series data reveal a consistent relationship between how much a technology’s cumulative installed capacity grows, and how long this growth takes. This relationship between extent (how much) and duration (for how long) is consistent across both energy supply and end-use technologies, and both established and emerging technologies. We then develop and test an approach for using this historical relationship to assess technological trajectories in future scenarios. Our approach for “learning from the past” contributes to the assessment and verification of integrated assessment and energy-economic models used to generate quantitative scenarios. Using data on power generation technologies from two such models, we also find a consistent extent - duration relationship across both technologies and scenarios. This relationship describes future low carbon technological growth in the power sector which appears to be conservative relative to what has been evidenced historically. Specifically, future extents of capacity growth are comparatively low given the lengthy time duration of that growth. We treat this finding with caution due to the low number of data points. Yet it remains counter-intuitive given the extremely rapid growth rates of certain low carbon technologies under stringent emission constraints. We explore possible reasons for the apparent scenario conservatism, and find parametric or structural conservatism in the underlying models to be one possible explanation.     

A tale of two population crises in recent Chinese history

The fall of the Ming dynasty in the first half of the 17th century and the Taiping Rebellion from 1851–1864 were two of the most chaotic periods in Chinese history, and each was accompanied by large-scale population collapses. The ‘Kang-Qian Golden Age’ (also known as ‘High Qing’), during which population size expanded rapidly, falls in between the two. Scholars remain divided in their opinions concerning the above alternation of population growth and decline as to whether variations in population size or climate change should be identified as the root cause. In either case, the synergistic impact of population growth and climate change upon population growth dynamics is overlooked. In the present study, we utilized high-resolution empirical data, qualitative survey, statistical comparison and time-series analysis to investigate how the two factors worked synergistically to drive population cycles in 1600–1899. To facilitate our research, we posited a set of simplified pathways for population growth in historical agrarian China. Our results confirm that the interrelation between population growth, climate change and population crises in recent Chinese history basically followed our posited pathways. The recurrences of population crises were largely determined by the combination of population growth and climate change. Our results challenge classic Malthusian/post-Malthusian interpretations and historians’ views of historical Chinese population cycles.     

Climate change and infectious diseases: Can we meet the needs for better prediction?

The next generation of climate-driven, disease prediction models will most likely require a mechanistically based, dynamical framework that parameterizes key processes at a variety of locations. Over the next two decades, consensus climate predictions make it possible to produce forecasts for a number of important infectious diseases that are largely independent of the uncertainty of longer-term emissions scenarios. In particular, the role of climate in the modulation of seasonal disease transmission needs to be unravelled from the complex dynamics resulting from the interaction of transmission with herd immunity and intervention measures that depend upon previous burdens of infection. Progress is also needed to solve the mismatch between climate projections and disease projections at the scale of public health interventions. In the time horizon of seasons to years, early warning systems should benefit from current developments on multi-model ensemble climate prediction systems, particularly in areas where high skill levels of climate models coincide with regions where large epidemics take place. A better understanding of the role of climate extremes on infectious diseases is urgently needed.     

From desert to Mediterranean rangelands: will increasing drought and inter-annual rainfall variability affect herbaceous annual primary productivity?

Climate change is predicted to alter the rainfall regime in the Eastern Mediterranean Basin: total annual rainfall will decrease, while seasonal and inter-annual variation in rainfall will increase. Such changes in the rainfall regime could potentially lead to large-scale changes in aboveground net primary productivity (ANPP) in the region. We conducted a data-driven evaluation of herbaceous ANPP along an entire regional rainfall gradient, from desert (90 mm MAR [Mean Annual Rainfall]) to Mesic-Mediterranean (780 mm MAR) ecosystems, using the largest database ever collated for herbaceous ANPP in Israel, with the aim of predicting consequences of climate change for rangeland productivity. This research revealed that herbaceous ANPP increases with increasing rainfall along the gradient, but strong dependence on rainfall was only apparent within dry sites. Rain Use Efficiency peaks at mid-gradient in Mediterranean sites without woody vegetation (560 and 610 mm MAR). Inter-annual coefficients of variation in rainfall and herbaceous ANPP decrease along the rainfall gradient up to ca. 500 mm MAR. Climate change is more likely to affect herbaceous ANPP of rangelands in the arid end of the rainfall gradient, requiring adaptation of rangeland management, while ANPP of rangelands in more mesic ecosystems is less responsive to variation in rainfall. We conclude that herbaceous ANPP in most Mediterranean rangelands is less vulnerable to climate change than generally predicted.