Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset

CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5 degrees latitude by 0.5 degrees longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901-2018 by the inclusion of additional station observations, and it will be updated annually. The interpolation process has been changed to use angular-distance weighting (ADW), and the production of secondary variables has been revised to better suit this approach. This implementation of ADW provides improved traceability between each gridded value and the input observations, and allows more informative diagnostics that dataset users can utilise to assess how dataset quality might vary geographically

Analysis of rainfall records from Dale Fort

A composite record of monthly rainfall is presented for Dale Fort Field Centre. The original observations were made in Haverfordwest from 1849 to 1909 and then a series from Stackpole Court was used for the period 1910-1970. The single homogenous series was produced by Dick Tabony at the UK Meteorological Office in 1980 since when the series has been extended using the Dale Fort observations. A daily rainfall record is available for Dale Fort from 1961 and this has been analysed to look at the frequency of measurable rainfall and heavy falls of rain. Some comparisons are made with rainfall inland where there is orographic enhancement of upland rainfall. The wettest day at Dale Fort was 11th October 2005 when exactly 92 mm was recorded

In Memoriam: Keith R. Briffa, 1952–2017

Keith R. Briffa was one of the most influential palaeoclimatologists of the last 30 years. His primary research interests lay in Late-Holocene climate change with a geographical emphasis on northern Eurasia. His greatest impact was in the field of dendroclimatology, a field that he helped to shape. His contributions have been seminal to the development of sound methods for tree-ring analysis and in their proper application to allow the interpretation of climate variability from tree rings. This led to the development of many important records that allow us to understand natural climate variability on timescales from years to millennia and to set recent climatic trends in their historical context

Response of the Asian summer Monsoons to a high-latitude thermal forcing: mechanisms and nonlinearities

This study investigates mechanisms and nonlinearities in the response of the Asian Summer Monsoons (ASM) to high-latitude thermal forcings of different amplitudes. Using a suite of runs carried out with an intermediate-complexity atmospheric general circulation model, we find that the imposed forcings produce a strong precipitation response over the eastern ASM but a rather weak response over the southern ASM. The forcing also causes a precipitation dipole with wet conditions over the eastern Tibetan Plateau (TP) and dry conditions over the Bay of Bengal (BoB) and southeast Asia. A moderate increase of precipitation along the southern margin of the TP is also produced. Simulations designed to isolate the causal mechanisms show that thermodynamic interactions involving the tropical surface oceans are far less important than the water-vapour feedback for the transmission of information from the high-latitudes to the ASM. Additionally, we assess the nonlinearity of the ASM precipitation response to the forcing amplitude using a novel application of the empirical orthogonal function method. The response can be decomposed in two overlapping patterns. The first pattern represents a precipitation dipole with wet conditions over the eastern TP and dry conditions over BoB, which linearly increases with forcing amplitude becoming quasi-stationary for large forcing amplitudes (i.e. amplitudes leading to Arctic temperature anomalies larger than 10 °C). The second pattern is associated with increased precipitation over the southeastern TP and is nonlinearly dependent on forcing, being most important for intermediate forcing amplitudes (i.e. amplitudes leading to Arctic temperature anomalies between 5 and 10 °C). © 2020, The Author(s)

Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain

The United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km regional perturbed physics ensemble (UKCP18-RCM-PPE) is one of the three strands of the latest set of UK national climate projections produced by the UK Met Office. It has been widely adopted in climate impact assessment. In this study, we report biases in the raw UKCP18-RCM simulations that are significant and are likely to deteriorate impact assessments if they are not adjusted. Two methods were used to bias-correct UKCP18-RCM: non-parametric quantile mapping using empirical quantiles and a variant developed for the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) designed to preserve the climate change signal. Specifically, daily temperature and precipitation simulations for 1981 to 2080 were adjusted for the 12 ensemble members. Potential evapotranspiration was also estimated over the same period using the Penman-Monteith formulation and then bias-corrected using the latter method. Both methods successfully corrected biases in a range of daily temperature, precipitation and potential evapotranspiration metrics, and reduced biases in multi-day precipitation metrics to a lesser degree. An exploratory analysis of the projected future changes confirms the expectation of wetter, warmer winters and hotter, drier summers, and shows uneven changes in different parts of the distributions of both temperature and precipitation. Both bias-correction methods preserved the climate change signal almost equally well, as well as the spread among the projected changes. The change factor method was used as a benchmark for precipitation, and we show that it fails to capture changes in a range of variables, making it inadequate for most impact assessments. By comparing the differences between the two bias-correction methods and within the 12 ensemble members, we show that the uncertainty in future precipitation and temperature changes stemming from the climate model parameterisation far outweighs the uncertainty introduced by selecting one of these two bias-correction methods. We conclude by providing guidance on the use of the bias-corrected data sets. The data sets bias adjusted with ISIMIP3BA are publicly available in the following repositories: https://doi.org/10.5281/zenodo.6337381 for precipitation and temperature (Reyniers et al., 2022a) and https://doi.org/10.5281/zenodo.6320707 for potential evapotranspiration (Reyniers et al., 2022b) . The datasets bias-corrected using the quantile mapping method are available at https://doi.org/10.5281/zenodo.8223024 (Zha et al., 2023)

Causes of East Asian temperature multidecadal variability since 850 CE

The drivers of multidecadal to centennial‐scale variability in East Asian temperature, apparent in temperature reconstructions, are poorly understood. Here, we apply a multivariate regression analysis to distinguish the influences of large‐scale modes of internal variability (Atlantic Multidecadal Oscillation, AMO; and Pacific Multidecadal Oscillation, PMO), and external natural (orbital, solar and volcanic) and anthropogenic (greenhouse gas concentrations, aerosols, and land use changes) forcings on East Asian warm‐season temperature over the period 850–1999 AD. We find that ~80% of the temperature change on timescales longer than 30 years can be explained including all drivers over the full‐length period. The PMO was the most important driver of multidecadal temperature variability during the Medieval Climate Anomaly (here, 950–1250), while solar contribution was important during the Little Ice Age (here, 1350–1850). Since 1850, two‐thirds of temperature change can be explained with anthropogenic forcing, whereas one‐third was related mainly to the AMO and volcanic forcing

The global and regional impacts of climate change under Representative Concentration Pathway forcings and Shared Socioeconomic Pathway socioeconomic scenarios

This paper presents an evaluation of the global and regional consequences of climate change for heat extremes, water resources, river and coastal flooding, droughts, agriculture and energy use. It presents change in hazard and resource base under different rates of climate change (Representative Concentration Pathways: RCP), and socio-economic impacts are estimated for each combination of RCP and Shared Socioeconomic Pathway. Uncertainty in the regional pattern of climate change is characterised by CMIP5 climate model projections. The analysis adopts a novel approach using relationships between level of warming and impact to rapidly estimate impacts under any climate forcing. The projections provided here can be used to inform assessments of the implications of climate change. At the global scale all the consequences of climate change considered here are adverse, with large increases under the highest rates of warming. Under the highest forcing the global average annual chance of a major heatwave increases from 5% now to 97% in 2100, the average proportion of time in drought increases from 7% to 27%, and the average chance of the current 50-year flood increases from 2% to 7%. The socio-economic impacts of these climate changes are determined by socio-economic scenario. There is variability in impact across regions, reflecting variability in projected changes in precipitation and temperature. The range in the estimated impacts can be large, due to uncertainty in future emissions and future socio-economic conditions and scientific uncertainty in how climate changes in response to future emissions. For the temperature-based indicators, the largest source of scientific uncertainty is in the estimated magnitude of equilibrium climate sensitivity, but for the indicators determined by precipitation the largest source is in the estimated spatial and seasonal pattern of changes in precipitation. By 2100 the range across socio-economic scenario is often greater than the range across the forcing levels

Projected changes in droughts and extreme droughts in Great Britain strongly influenced by the choice of drought index

Droughts cause enormous ecological, economical and societal damage, and they are already undergoing changes due to anthropogenic climate change. The issue of defining and quantifying droughts has long been a substantial source of uncertainty in understanding observed and projected trends. Atmosphere-based drought indicators, such as the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI), are often used to quantify drought characteristics and their changes, sometimes as the sole metric representing drought. This study presents a detailed systematic analysis of SPI- and SPEI-based drought projections and their differences for Great Britain (GB), derived from the most recent set of regional climate projections for the United Kingdom (UK). We show that the choice of drought indicator has a decisive influence on the resulting projected changes in drought frequency, extent, duration and seasonality using scenarios that are 2 and 4 ∘C above pre-industrial levels. The projected increases in drought frequency and extent are far greater based on the SPEI than based on the SPI. Importantly, compared with droughts of all intensities, isolated extreme droughts are projected to increase far more with respect to frequency and extent and are also expected to show more pronounced changes in the distribution of their event durations. Further, projected intensification of the seasonal cycle is reflected in an increasing occurrence of years with (extremely) dry summers combined with wetter-than-average winters. Increasing summer droughts also form the main contribution to increases in annual droughts, especially using the SPEI. These results show that the choice of atmospheric drought index strongly influences the drought characteristics inferred from climate change projections, with a comparable impact to the uncertainty from the climate model parameters or the warming level; therefore, potential users of these indices should carefully consider the importance of potential evapotranspiration in their intended context. The stark differences between SPI- and SPEI-based projections highlight the need to better understand the interplay between increasing atmospheric evaporative demand, moisture availability and drought impacts under a changing climate. The region-dependent projected changes in drought characteristics by two warming levels have important implications for adaptation efforts in GB, and they further stress the need for rapid mitigation

Pattern scaling using ClimGen: monthly-resolution future climate scenarios including changes in the variability of precipitation

Development, testing and example applications of the pattern-scaling approach for generating future climate change projections are reported here, with a focus on a particular software application called “ClimGen”. A number of innovations have been implemented, including using exponential and logistic functions of global-mean temperature to represent changes in local precipitation and cloud cover, and interpolation from climate model grids to a finer grid while taking into account land-sea contrasts in the climate change patterns. Of particular significance is a new approach for incorporating changes in the inter-annual variability of monthly precipitation simulated by climate models. This is achieved by diagnosing simulated changes in the shape of the gamma distribution of monthly precipitation totals, applying the pattern-scaling approach to estimate changes in the shape parameter under a future scenario, and then perturbing sequences of observed precipitation anomalies so that their distribution changes according to the projected change in the shape parameter. The approach cannot represent changes to the structure of climate timeseries (e.g. changed autocorrelation or teleconnection patterns) were they to occur, but is shown here to be more successful at representing changes in low precipitation extremes than previous pattern-scaling methods