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PDRMIP investigates the role of various drivers of climate change for mean and extreme precipitation changes, based on multiple climate model output and energy budget analyses.

As the global temperature increases with changing climate, precipitation rates and patterns are affected through a wide range of physical mechanisms. The globally averaged intensity of extreme precipitation also changes more rapidly than the globally averaged precipitation rate. While some aspects of the regional variation in precipitation predicted by climate models appear robust, there is still a large degree of inter-model differences unaccounted for. Individual drivers of climate change initially alter the energy budget of the atmosphere leading to distinct rapid adjustments involving changes in precipitation. Differences in how these rapid adjustment processes manifest themselves within models are likely to explain a large fraction of the present model spread and needs better quantifications to improve precipitation predictions. Here, we introduce the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where a set of idealized experiments designed to understand the role of different climate forcing mechanisms were performed by a large set of climate models. PDRMIP focuses on understanding how precipitation changes relating to rapid adjustments and slower responses to climate forcings are represented across models. Initial results show that rapid adjustments account for large regional differences in hydrological sensitivity across multiple drivers. The PDRMIP results are expected to dramatically improve our understanding of the causes of the present diversity in future climate projections.

This is the Pacific Climate Impacts Constortium's 2015-2016 Corporate Report.

While it is generally accepted that the observed reduction of the Northern Hemisphere spring snow cover extent (SCE) is linked to warming of the climate system caused by human induced greenhouse gas emissions, it has been difficult to robustly quantify the anthropogenic contribution to the observed change. This study addresses the challenge by undertaking a formal detection and attribution analysis of SCE changes based on several observational datasets with different structural characteristics, in order to account for the substantial observational uncertainty. The datasets considered include a blended in situ-satellite dataset extending from 1923 to 2012 (Brown), the National Oceanic and Atmospheric Administration (NOAA) snow chart Climate Data Record for 1968–2012, the Global Land Data Assimilation System version 2.0 (GLDAS-2 Noah) reanalysis for 1951–2010, and the NOAA 20th-century reanalysis, version 2 (20CR2) covering 1948–2012. We analyse observed early spring (March-April) and late spring (May-June) NH SCE extent changes in these datasets using climate simulations of the responses to anthropogenic and natural forcings combined (ALL) and to natural forcings alone (NAT) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The ALL-forcing response is detected in all of the observed records, indicating that observed changes are inconsistent with internal variability. The analysis also shows that the ALL-forcing simulations substantially underestimate the observed changes as recorded in the Brown and NOAA datasets, but that they are more consistent with changes seen in the GLDAS and 20CR2 reanalyses. A two-signal analysis of the GLDAS data is able to detect the influence of the anthropogenic component of the observed SCE changes separately from the effect of natural forcing. Despite dataset and modelling uncertainty, these results, together with the understanding of the causes of observed warming over the past century, provide substantial evidence of a human contribution to the observed decline in Northern Hemisphere spring snow cover extent.

The temporal variability of river and soil water affects society at time scales ranging from hourly to decadal. The available water (AW), i.e., precipitation minus evapotranspiration, represents the total water available for runoff, soil water storage change, and ground water recharge. The reliability of AW is defined as the annual range of AW between local wet and dry seasons. A smaller annual range represents greater reliability and a larger range denotes less reliability. Here we assess the reliability of AW in the 21st century climate projections by 20 climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The multimodel consensus suggests less reliable AW in the 21st century than in the 20th century with generally decreasing AW in local dry seasons and increasing AW in local wet seasons. In addition to the canonical perspective from climate models that wet regions will get wetter, this study suggests greater dryness during dry seasons even in regions where the mean climate becomes wetter. Lower emission scenarios show significant advantages in terms of minimizing impacts on AW but do not eliminate these impacts altogether.

China has warmed rapidly over the past half century and has experienced widespread concomitant impacts on water availability, agriculture and ecosystems. Although urban areas occupy less than 1% of China’s land mass, the majority of China’s observing stations are situated in proximity to urban areas, and thus some of the recorded warming is undoubtedly the consequence of rapid urban development, particularly since the late 1970s. Here, we quantify the separate contributions of urbanization and other external forcings to the observed warming. We estimate that China’s temperature increased by 1.44 °C (90% confidence interval 1.22–1.66 °C) over the period 1961–2013 and that urban warming influences account for about a third of this observed warming, 0.49 °C (0.12–0.86 °C). Anthropogenic and natural external forcings combined explain most of the rest of the observed warming, contributing 0.93 °C (0.61–1.24 °C). This is close to the warming of 1.09 °C (0.86–1.31 °C) observed in global mean land temperatures over the period 1951–2010, which, in contrast to China’s recorded temperature change, is only weakly affected by urban warming influences. Clearly the effects of urbanization have considerably exacerbated the warming experienced by the large majority of the Chinese population in comparison with the warming that they would have experienced as a result of external forcing alone.

The Indo-Pacific warm pool (IPWP) has warmed and grown substantially during the past century. The IPWP is Earth’s largest region of warm sea surface temperatures (SSTs), has the highest rainfall, and is fundamental to global atmospheric circulation and hydrological cycle. The region has also experienced the world’s highest rates of sea-level rise in recent decades, indicating large increases in ocean heat content and leading to substantial impacts on small island states in the region. Previous studies have considered mechanisms for the basin-scale ocean warming, but not the causes of the observed IPWP expansion, where expansion in the Indian Ocean has far exceeded that in the Pacific Ocean. We identify human and natural contributions to the observed IPWP changes since the 1950s by comparing observations with climate model simulations using an optimal fingerprinting technique. Greenhouse gas forcing is found to be the dominant cause of the observed increases in IPWP intensity and size, whereas natural fluctuations associated with the Pacific Decadal Oscillation have played a smaller yet significant role. Further, we show that the shape and impact of human-induced IPWP growth could be asymmetric between the Indian and Pacific basins, the causes of which remain uncertain. Human-induced changes in the IPWP have important implications for understanding and projecting related changes in monsoonal rainfall, and frequency or intensity of tropical storms, which have profound socioeconomic consequences.

This newsletter covers the climate impacts summaries for the City of Vancouver and Whistler, updates to the PCIC Publications Library, the National Academy of Sciences' report on extreme weather, conferences attended by PCIC researchers, the last talk of the 2015-016 Pacific Climate Seminar Series, staff changes at PCIC and recent papers authored by PCIC researchers.

We evaluate the capacity of a regional climate model to represent observed extreme temperature and precipitation events and also examine the impact of increased resolution, in an effort to identify added value in this respect. Two climate simulations of western Canada (WCan) were conducted with the Canadian Regional Climate Model (version 4) at 15 (CRCM15) and 45 km (CRCM45) horizontal resolution driven at the lateral boundaries by data from the European Centre for Medium-range Weather Forecasts (ECMWF) 40-year Reanalysis (ERA-40) for the period 1973–1995. The simulations were evaluated using the spline-interpolated dataset ANUSPLIN, a daily observational gridded surface temperature and precipitation product with a nominal resolution of approximately 10 km. We examine a range of climate extremes, comprising the 10th and 90th percentiles of daily maximum (TX) and minimum (TN) temperatures, the 90th percentile of daily precipitation (PR90), and the 27 core Climate Daily Extremes (CLIMDEX) indices.

Both simulations exhibit cold biases compared with observations over WCan, with the bias exacerbated at higher resolution, suggesting little added value for temperature overall. There are instances, however, of regional improvement in the spatial pattern of temperature extremes at the higher resolution of CRCM15 (e.g., the CLIMDEX index for the annual number of days when TX > 25°C). The high-resolution simulations also reveal similarly localized features in precipitation (e.g., rain shadows) that are not resolved at the 45 km resolution. With regard to precipitation extremes, although both simulations generally display wet biases, CRCM15 features a reduced bias in PR90 in all seasons except winter. This improvement occurs despite the fact that spatial and interannual variability of PR90 in CRCM15 is significantly overestimated relative to both CRCM45 and ANUSPLIN. We posit that these characteristics are the result of demonstrable differences between corresponding topographical datasets used in the gridded observations and CRCM, the resulting errors propagated to physical variables tied to elevation and the beneficial effect of subsequent spatial averaging. Because topographical input is often discordant between simulations and gridded observations, it is argued that a limited form of spatial averaging may contribute added value beyond that which has already been noted in previous studies with respect to small-scale climate variability.