Search

Most studies into the effects of climate change have headline results in the form of a global change in mean temperature. More useful for businesses and governments, however, are measures of the localized impact, and also of extremes rather than averages. We have addressed this by examining the change in frequency of exceeding a daily mean temperature threshold, defined as ‘disruption days’, as it is often this exceedance which has the most dramatic impacts on personal or economic behaviour. Our exceedance analysis tackles the resolution of climate change both geographically and temporally, the latter specifically to address the 5- to 20-year time horizon which can be recognized in business planning. We apply bias correction with quantile mapping to meteorological reanalysis data from ECMWF ERA5 and output from CMIP5 climate model simulations. By determining the daily frequency at which a mean temperature threshold is exceeded in this bias-corrected dataset, we can compare predicted and historic frequencies to estimate the change in the number of disruption days. Furthermore, by combining results from 18 different climate models, we can estimate the likelihood of more extreme events, taking into account model variations. This is useful for worst-case scenario planning. Taking the city of Chicago as an example, the expected frequency of years with 40 or more disruption days above the 25°C threshold rises by a factor of four for a time period centred on 2040, compared with a period centred on 2000. Alternately, looking at the change in the number of days at a given likelihood, an example is Shenzhen, where the number of disruption days in a once-per-decade event exceeding the 25°C or 30°C threshold is expected to rise by a factor of four. In a future stage, superimposing these results onto maps of, for instance, GDP sensitivity or production days lost, will provide more accurate and targeted conclusions for future impacts of climate change. This method of quantifying costs on business-relevant timescales will enable businesses and governments properly include risks associated with facilities, plan mitigating actions and make accurate provisions. It can also, for example, inform their disclosure of physical risks under the framework of the Task Force on Climate-related Financial Disclosures. This approach is equally applicable to other weather-related, localized phenomena likely to be impacted by climate change.

We investigate variability and trends of the Southern Hemisphere quasi-stationary planetary waves over 1979–2013 using the European Centre for Medium-range Weather Forecasts (ECMWF) Interim reanalyses. The effects of tropical and extra-tropical forcing factors on the phase and amplitude of the planetary waves are identified. The amplitudes of wave numbers 1–3 exhibit an annual cycle with a minimum in summer and maximum over the extended austral winter period. The phase of wave number 1 has a semi-annual cycle, moving east in austral spring/autumn and west in summer/winter as a result of differences in the phase of the semi-annual oscillation across the Pacific sector of the Southern Ocean. The phase of wave number 3 has an annual cycle, being more eastward (westward) in summer (winter). Year-to-year variability of the amplitude of wave number 1 is found to be strongly associated with the Amundsen Sea Low, which in turn is known to be strongly influenced by the El Niño–Southern Oscillation, with the consequence that the amplitude of wave number 1 is larger during the El Niño phase of the cycle. Regarding trends for the year as a whole, the amplitude of wave number 1 has decreased since 1979 (p < 0.1), while the amplitudes of wave numbers 2 and 3 have increased. These changes are consistent with the warming trends in sea surface temperatures across much of the tropical oceans. However, the factors associated with longer-term trends are less clear than for year-to-year variability.

Despite the recent significant climatic changes observed over West Antarctica, which include large warming in central West Antarctica and accelerated ice loss, adequate validation of regional simulations of meteorological variables are rare for this region. To address this gap, results from a recent version of the Polar Weather Research and Forecasting model (Polar WRF) covering West Antarctica at a high horizontal resolution of 5 km were validated against near-surface meteorological observations. The model employed physics options that included the Mellor-Yamada-Nakanishi-Niino (MYNN) boundary layer scheme, the WRF Single Moment 5-Class cloud microphysics scheme, the new version of the Rapid Radiative Transfer Model for both shortwave and longwave radiation, and the Noah land surface model. Our evaluation finds this model to be a useful tool for realistically capturing the near-surface meteorological conditions. It showed high skill in simulating surface pressure (correlation ≥0.97), good skill for wind speed with better correlation at inland sites (0.7-0.8) compared to coastal sites (0.3-0.6), generally good representation of strong wind events, and good skill for temperature in winter (correlation ≥0.8). The main shortcomings of this configuration of Polar WRF are an occasional failure to properly represent transient cyclones and their influence on coastal winds, an amplified diurnal temperature cycle in summer, and a general tendency to underestimate the wind speed at inland sites in summer. Additional sensitivity studies were performed to quantify the impact of the choice of boundary layer scheme and surface boundary conditions. It is shown that the model is most sensitive to the choice of boundary layer scheme, with the representation of the temperature diurnal cycle in summer significantly improved by selecting the Mellor-Yamada-Janjic boundary layer scheme. By contrast, the model results showed little sensitivity to whether the horizontal resolution was 5 or 15 km.

This study seeks to quantify the influence of South Georgia's orography on regional surface winds. A typical case study characterized by large-scale westerly winds is analysed using a high-resolution setup (3.3 km) of the Weather Research and Forecasting (WRF) regional model. The simulation produces significant fine-scale spatial variability which is in agreement with satellite-derived winds. The model simulation indicates that these orography-driven wind disturbances are responsible for strong wind stress curl and enhanced heat flux over the shelf waters surrounding South Georgia. Such surface forcing is entirely absent from the reanalysis, highlighting the need to use high-resolution forcing in regional ocean model simulations

On 24 September 2012 the Antarctic sea ice extent (SIE) reached a new annual daily maximum (ADM) for the satellite era of 19.72 × 106 km2. The largest positive SIE anomalies compared to the mean of all ADMs were found over the northern Amundsen Sea, off the coast of Wilkes Land, with smaller positive anomalies off the Dronning Maud Land coast (30° W to 30° E). The SIE at the ADM is significantly correlated with the extents for the previous 80 days, but the ice growth during the winter of 2012 was close to the climatological rate and one month before the ADM the SIE was near the recent mean. Deep depressions in the circumpolar trough since late August 2012 resulted in strong southerly flow and marked northward sea ice advection. The linear trend in SIE suggests that it contributed ~40% to the 2012 anomaly, with depression activity adding ~60%.

We develop a climatology of the Amundsen Sea low (ASL) covering the period 1979–2008 using ECMWF operational and reanalysis fields. The depth of the ASL is strongly influenced by the phase of the Southern annular mode (SAM) with positive (negative) mean sea level pressure anomalies when the SAM is negative (positive). The zonal location of the ASL is linked to the phase of the mid-tropospheric planetary waves and the low moves west from close to 110°W in January to near 150°W in June as planetary waves 1 to 3 amplify and their phases shift westwards. The ASL is deeper by a small, but significant amount, during the La Niña phase of El Niño-Southern Oscillation (ENSO) compared to El Niño. The difference in depth of the low between the two states of ENSO is greatest in winter. There is no statistically significant difference in the zonal location of the ASL between the different phases of ENSO. Over 1979–2008 the low has deepened in January by 1.7 hPa dec−1 as the SAM has become more positive. It has also deepened in spring and autumn as the semi-annual oscillation has increase in amplitude over the last 30 years. An increase in central pressure and eastward shift in March has occurred as a result of a cooling of tropical Pacific SSTs that altered the strength of the polar front jet.