Climate change and water in the UK: past changes and future prospects

Climate change is expected to modify rainfall, temperature and catchment hydrological responses across the world, and adapting to these water-related changes is a pressing challenge. This paper reviews the impact of anthropogenic climate change on water in the UK and looks at projections of future change. The natural variability of the UK climate makes change hard to detect; only historical increases in air temperature can be attributed to anthropogenic climate forcing, but over the last 50 years more winter rainfall has been falling in intense events. Future changes in rainfall and evapotranspiration could lead to changed flow regimes and impacts on water quality, aquatic ecosystems and water availability. Summer flows may decrease on average, but floods may become larger and more frequent. River and lake water quality may decline as a result of higher water temperatures, lower river flows and increased algal blooms in summer, and because of higher flows in the winter. In communicating this important work, researchers should pay particular attention to explaining confidence and uncertainty clearly. Much of the relevant research is either global or highly localized: decision-makers would benefit from more studies that address water and climate change at a spatial and temporal scale appropriate for the decisions they make

Observed relationships between maize yield and climate in Sri Lanka

The present study evaluated the effects of climate variability on maize (Zea mays L.) yield in Sri Lanka at different spatial scales. Biophysical data from the Department of Agriculture (DOA) in Sri Lanka for six major maize-growing districts (Ampara, Anuradhapura, Badulla, Hambantota, Moneragala, and Kurunegala) from 1990 to 2010 were analyzed. Simple linear regression models were fitted to observed climate data and detrended maize yield to identify significant correlations. The correlation between first differences of maize yield and climate (r) was further investigated at 0.50° grid scale using interpolated climate data. After 2003, significantly positive (p < 0.01) yield trends varied from 154 kg ha–1 yr–1 to 360 kg ha–1 yr–1. The correlations between maize yield and climate reported that five out of six districts were significant at 10% level. Rainfall had a consistent significant (p < 0.10) positive impact on maize yield in Anuradhapura, Hambantota, and Moneragala, where seasonal total rainfall together with high temperature (“hot-dry”) are the key limitations. Further, the seasonal mean temperature had a negative impact on maize yield in Moneragala (“hot-dry”), the only district that showed high temperatures. Badulla district (“cold-dry”) reported a significant (r = 0.38) positive correlation with mean seasonal temperature, indicating higher potential toward increasing temperatures. Each 1°C rise in seasonal mean temperature reduced maize yield by about 5% from 1990 to 2010. Overall, there was a reasonable correlation between district maize yield and seasonal climate in most of the districts within the maize belt of Sri Lanka.

Vertical structure and physical processes of the Madden-Julian oscillation: exploring key model physics in climate simulations

Aimed at reducing deficiencies in representing the Madden-Julian oscillation (MJO) in general circulation models (GCMs), a global model evaluation project on vertical structure and physical processes of the MJO was coordinated. In this paper, results from the climate simulation component of this project are reported. It is shown that the MJO remains a great challenge in these latest generation GCMs. The systematic eastward propagation of the MJO is only well simulated in about one-fourth of the total participating models. The observed vertical westward tilt with altitude of the MJO is well simulated in good MJO models, but not in the poor ones. Damped Kelvin wave responses to the east of convection in the lower troposphere could be responsible for the missing MJO preconditioning process in these poor MJO models. Several process-oriented diagnostics were conducted to discriminate key processes for realistic MJO simulations. While large-scale rainfall partition and low-level mean zonal winds over the Indo-Pacific in a model are not found to be closely associated with its MJO skill, two metrics, including the low-level relative humidity difference between high and low rain events and seasonal mean gross moist stability, exhibit statistically significant correlations with the MJO performance. It is further indicated that increased cloud-radiative feedback tends to be associated with reduced amplitude of intraseasonal variability, which is incompatible with the radiative instability theory previously proposed for the MJO. Results in this study confirm that inclusion of air-sea interaction can lead to significant improvement in simulating the MJO.

Advances in the stochastic modelling of satellite-derived rainfall estimates using a sparse calibration dataset

As satellite technology develops, satellite rainfall estimates are likely to become ever more important in the world of food security. It is therefore vital to be able to identify the uncertainty of such estimates and for end users to be able to use this information in a meaningful way. This paper presents new developments in the methodology of simulating satellite rainfall ensembles from thermal infrared satellite data. Although the basic sequential simulation methodology has been developed in previous studies, it was not suitable for use in regions with more complex terrain and limited calibration data. Developments in this work include the creation of a multithreshold, multizone calibration procedure, plus investigations into the causes of an overestimation of low rainfall amounts and the best way to take into account clustered calibration data. A case study of the Ethiopian highlands has been used as an illustration.

A regime-dependent parametrization of subgrid-scale cloud water content variability

The subgrid-scale spatial variability in cloud water content can be described by a parameter f called the fractional standard deviation. This is equal to the standard deviation of the cloud water content divided by the mean. This parameter is an input to schemes that calculate the impact of subgrid-scale cloud inhomogeneity on gridbox-mean radiative fluxes and microphysical process rates. A new regime-dependent parametrization of the spatial variability of cloud water content is derived from CloudSat observations of ice clouds. In addition to the dependencies on horizontal and vertical resolution and cloud fraction included in previous parametrizations, the new parametrization includes an explicit dependence on cloud type. The new parametrization is then implemented in the Global Atmosphere 6 (GA6) configuration of the Met Office Unified Model and used to model the effects of subgrid variability of both ice and liquid water content on radiative fluxes and autoconversion and accretion rates in three 20-year atmosphere-only climate simulations. These simulations show the impact of the new regime-dependent parametrization on diagnostic radiation calculations, interactive radiation calculations and both interactive radiation calculations and in a new warm microphysics scheme. The control simulation uses a globally constant f value of 0.75 to model the effect of cloud water content variability on radiative fluxes. The use of the new regime-dependent parametrization in the model results in a global mean which is higher than the control's fixed value and a global distribution of f which is closer to CloudSat observations. When the new regime-dependent parametrization is used in radiative transfer calculations only, the magnitudes of short-wave and long-wave top of atmosphere cloud radiative forcing are reduced, increasing the existing global mean biases in the control. When also applied in a new warm microphysics scheme, the short-wave global mean bias is reduced.

Spatial variability of liquid cloud and rain: observations and microphysical effects

Spatial variability of liquid cloud water content and rainwater content is analysed from three different observational platforms: in situ measurements from research aircraft, land-based remote sensing techniques using radar and lidar, and spaceborne remote sensing from CloudSat. The variance is found to increase with spatial scale, but also depends strongly on the cloud or rain fraction regime, with overcast regions containing less variability than broken cloud fields. This variability is shown to lead to large biases, up to a factor of 4, in both the autoconversion and accretion rates estimated at a model grid scale of ≈40 km by a typical microphysical parametrization using in-cloud mean values. A parametrization for the subgrid variability of liquid cloud and rainwater content is developed, based on the observations, which varies with both the grid scale and cloud or rain fraction, and is applicable for all model grid scales. It is then shown that if this parametrization of the variability is analytically incorporated into the autoconversion and accretion rate calculations, the bias is significantly reduced.

Mid- to late Holocene Indian Ocean monsoon variability recorded in four speleothems from Socotra Island, Yemen

Four stalagmites covering the last 7.0 ka were sampled on Socotra, an island in the northern Indian Ocean to investigate the evolution of the northeast Indian Ocean Monsoon (IOM) since the mid Holocene. On Socotra, rain is delivered at the start of the southwest IOM in May–June and at the start of the northeast IOM from September to December. The Haggeher Mountains act as a barrier forcing precipitation brought by the northeast winds to fall preferentially on the eastern side of the island, where the studied caves are located. δ18O and δ13C and Mg/Ca and Sr/Ca signals in the stalagmites reflect precipitation amounts brought by the northeast winds. For stalagmite STM6, this amount effect is amplified by kinetic effects during calcite deposition. Combined interpretation of the stalagmites' signals suggest a weakening of the northeast precipitation between 6.0 and 3.8 ka. After 3.8 ka precipitation intensities remain constant with two superimposed drier periods, between 0 and 0.6 ka and from 2.2 to 3.8 ka. No link can be established with Greenland ice cores and with the summer IOM variability. In contrast to the stable northeast rainy season suggested by the records in this study, speleothem records from western Socotra indicate a wettening of the southwest rainy season on Socotra after 4.4 ka. The local wettening of western Socotra could relate to a more southerly path (more over the Indian Ocean) taken by the southwest winds. Stalagmite STM5, sampled at the fringe between both rain areas displays intermediate δ18O values. After 6.2 ka, similar precipitation changes are seen between eastern Socotra and northern Oman indicating that both regions are affected similarly by the monsoon. Different palaeoclimatologic records from the Arabian Peninsula currently located outside the ITCZ migration pathway display an abrupt drying around 6 ka due to their disconnection from the southwest rain influence. Records that are nowadays still receiving rain by the southwest winds, suggest a more gradual drying reflecting the weakening of the southwest monsoon.

A radar-based rainfall climatology of Great Britain and Ireland

The Met Office 1km radar-derived precipitation-rate composite over 8 years (2006–2013) is examined to evaluate whether it provides an accurate representation of annual-average precipitation over Great Britain and Ireland over long periods of time. The annual-average precipitation from the radar composite is comparable with gauge measurements, with an average error of +23mmyr−1 over Great Britain and Ireland, +29mmyr−1 (3%) over the United Kingdom and –781mmyr−1 (46%) over the Republic of Ireland. The radar-derived precipitation composite is useful over the United Kingdom including Northern Ireland, but not accurate over the Republic of Ireland, particularly in the south.

Investigation of the accuracy of historical irradiance products and interannual variability of solar irradiance using Met Office ground data

Met Office station data from 1980 to 2012 has been used to characterise the interannual variability of incident solar irradiance across the UK. The same data are used to evaluate four popular historical irradiance products to determine which are most suitable for use by the UK PV industry for site selection and system design. The study confirmed previous findings that interannual variability is typically 3–6% and weighted average probability of a particular percentage deviation from the mean at an average site in the UK was calculated. This weighted average showed that fewer than 2% of site-years could be expected to fall below 90% of the long-term site mean. The historical irradiance products were compared against Met Office station data from the input years of each product. This investigation has found that all products perform well. No products have a strong spatial trend. Meteonorm 7 is most conservative (MBE = −2.5%), CMSAF is most optimistic (MBE = +3.4%) and an average of all four products performs better than any one individual product (MBE = 0.3%)

Atmospheric rivers do not explain UK summer extreme rainfall

Extreme rainfall events continue to be one of the largest natural hazards in the UK. In winter, heavy precipitation and floods have been linked with intense moisture transport events associated with atmospheric rivers (ARs), yet no large-scale atmospheric precursors have been linked to summer flooding in the UK. This study investigates the link between ARs and extreme rainfall from two perspectives: 1) Given an extreme rainfall event, is there an associated AR? 2) Given an AR, is there an associated extreme rainfall event? We identify extreme rainfall events using the UK Met Office daily rain-gauge dataset and link these to ARs using two different horizontal resolution atmospheric datasets (ERA-Interim and 20th Century Re-analysis). The results show that less than 35% of winter ARs and less than 15% of summer ARs are associated with an extreme rainfall event. Consistent with previous studies, at least 50% of extreme winter rainfall events are associated with an AR. However, less than 20% of the identified summer extreme rainfall events are associated with an AR. The dependence of the water vapor transport intensity threshold used to define an AR on the years included in the study, and on the length of the season, is also examined. Including a longer period (1900-2012) compared to previous studies (1979-2005) reduces the water vapor transport intensity threshold used to define an AR.

Exploring the impact of CMIP5 model biases on the simulation of North Atlantic decadal variability

Instrumental observations, palaeo-proxies, and climate models suggest significant decadal variability within the North Atlantic subpolar gyre (NASPG). However, a poorly sampled observational record and a diversity of model behaviours mean that the precise nature and mechanisms of this variability are unclear. Here, we analyse an exceptionally large multi-model ensemble of 42 present-generation climate models to test whether NASPG mean state biases systematically affect the representation of decadal variability. Temperature and salinity biases in the Labrador Sea co-vary and influence whether density variability is controlled by temperature or salinity variations. Ocean horizontal resolution is a good predictor of the biases and the location of the dominant dynamical feedbacks within the NASPG. However, we find no link to the spectral characteristics of the variability. Our results suggest that the mean state and mechanisms of variability within the NASPG are not independent. This represents an important caveat for decadal predictions using anomaly-assimilation methods.

A mechanism of internal decadal atlantic ocean variability in a high-resolution coupled climate model

The North Atlantic Ocean subpolar gyre (NA SPG) is an important region for initialising decadal climate forecasts. Climate model simulations and palaeo climate reconstructions have indicated that this region could also exhibit large, internally generated variability on decadal timescales. Understanding these modes of variability, their consistency across models, and the conditions in which they exist, is clearly important for improving the skill of decadal predictions — particularly when these predictions are made with the same underlying climate models. Here we describe and analyse a mode of internal variability in the NA SPG in a state-of-the-art, high resolution, coupled climate model. This mode has a period of 17 years and explains 15–30% of the annual variance in related ocean indices. It arises due to the advection of heat content anomalies around the NA SPG. Anomalous circulation drives the variability in the southern half of the NA SPG, whilst mean circulation and anomalous temperatures are important in the northern half. A negative feedback between Labrador Sea temperatures/densities and those in the North Atlantic Current is identified, which allows for the phase reversal. The atmosphere is found to act as a positive feedback on to this mode via the North Atlantic Oscillation which itself exhibits a spectral peak at 17 years. Decadal ocean density changes associated with this mode are driven by variations in temperature, rather than salinity — a point which models often disagree on and which we suggest may affect the veracity of the underlying assumptions of anomaly-assimilating decadal prediction methodologies.

Response of the North Atlantic wave climate to atmospheric modes of variability

This study investigates the relationship between the wind wave climate and the main climate modes of atmospheric variability in the North Atlantic Ocean. The modes considered are the North Atlantic Oscillation (NAO), the East Atlantic (EA) pattern, the East Atlantic Western Russian (EA/WR) pattern and the Scandinavian (SCAN) pattern. The wave dataset consists of buoys records, remote sensing altimetry observations and a numerical hindcast providing significant wave height (SWH), mean wave period (MWP) and mean wave direction (MWD) for the period 1989–2009. After evaluating the reliability of the hindcast, we focus on the impact of each mode on seasonal wave parameters and on the relative importance of wind-sea and swell components. Results demonstrate that the NAO and EA patterns are the most relevant, whereas EA/WR and SCAN patterns have a weaker impact on the North Atlantic wave climate variability. During their positive phases, both NAO and EA patterns are related to winter SWH at a rate that reaches 1 m per unit index along the Scottish coast (NAO) and Iberian coast (EA) patterns. In terms of winter MWD, the two modes induce a counterclockwise shift of up to 65° per negative NAO (positive EA) unit over west European coasts. They also increase the winter MWP in the North Sea and in the Bay of Biscay (up to 1 s per unit NAO) and along the western coasts of Europe and North Africa (1 s per unit EA). The impact of winter EA pattern on all wave parameters is mostly caused through the swell wave component.

Climate variability and human impact in South America during the last 2000 years: synthesis and perspectives from pollen records

An improved understanding of present-day climate variability and change relies on high-quality data sets from the past 2 millennia. Global efforts to model regional climate modes are in the process of being validated against, and integrated with, records of past vegetation change. For South America, however, the full potential of vegetation records for evaluating and improving climate models has hitherto not been sufficiently acknowledged due to an absence of information on the spatial and temporal coverage of study sites. This paper therefore serves as a guide to high-quality pollen records that capture environmental variability during the last 2 millennia. We identify 60 vegetation (pollen) records from across South America which satisfy geochronological requirements set out for climate modelling, and we discuss their sensitivity to the spatial signature of climate modes throughout the continent. Diverse patterns of vegetation response to climate change are observed, with more similar patterns of change in the lowlands and varying intensity and direction of responses in the highlands. Pollen records display local-scale responses to climate modes; thus, it is necessary to understand how vegetation–climate interactions might diverge under variable settings. We provide a qualitative translation from pollen metrics to climate variables. Additionally, pollen is an excellent indicator of human impact through time. We discuss evidence for human land use in pollen records and provide an overview considered useful for archaeological hypothesis testing and important in distinguishing natural from anthropogenically driven vegetation change. We stress the need for the palynological community to be more familiar with climate variability patterns to correctly attribute the potential causes of observed vegetation dynamics. This manuscript forms part of the wider LOng-Term multi-proxy climate REconstructions and Dynamics in South America – 2k initiative that provides the ideal framework for the integration of the various palaeoclimatic subdisciplines and palaeo-science, thereby jump-starting and fostering multidisciplinary research into environmental change on centennial and millennial timescales.