“Agro-meteorological indices and climate model uncertainty over the UK”

Abstract

Five stakeholder-relevant indices of agro-meteorological change were analysed for the UK, over past (1961-1990) and future (2061-2090) periods. Accumulated Frosts, Dry Days, Growing Season Length, Plant Heat Stress and Start of Field Operations were calculated from the E-Obs (European Observational) and HadRM3 (Hadley Regional Climate Model) PPE (perturbed physics ensemble) data sets. Indices were compared directly and examined for current and future uncertainty. Biases are quantified in terms of ensemble member climate sensitivity and regional aggregation. Maps of spatial change then provide an appropriate metric for end-users both in terms of their requirements and statistical robustness. A future UK is described with fewer frosts, fewer years with a large number of frosts, an earlier start to field operations (e.g., tillage), fewer occurrences of sporadic rainfall, more instances of high temperatures (in both the mean and upper range), and a much longer growing season. 1 Introduction Decision making for climate change adaptation within the land use sector, and appropriate policy development, requires consideration of relevant environmental pressures (Matthews et al. 2008). The variability of key weather-driven factors, thresholds and tolerances must be identified to form the basis for land manager decision-making (Matthews et al. 2008; McCrum et al. 2009; Rivington et al. 2008a, 2013). In the UK 70 % of land-use is agricultural (DEFRA 2011), only 6.8 % can be classified as "urban" (NEA 2012). When in-urban "greenspaces" and water surfaces are accounted for, the UK's built environment drops to roughly 2.3 % of landuse, with less than 0.5 % for Scotland (NEA 2012). Land management invested individuals and agencies are thus highly exposed to environmental change (Kane et al. 1992). Prior studies have focussed largely on metrics of drought (Keyantash and Dracup 2002; Piao et al. 2010) as the most costly form of discrete climate impact (Keyantash and Dracup

Probabilistic soil moisture projections to assess Great Britain’s future clay-related subsidence hazard

“…Therefore, we recommend that the NPSs for infrastructure resilience be amended to more fully incorporate potential climate change and environmental vulnerabilities. Harding et al (2014) argue that end-users of climate model data are predominantly interested in the magnitude of change that is likely to be experienced. The probabilistic projections of clay subsidence provided (Fig.…”

“…Kay and Jones (2012) argue that comparison of WG outputs and empirically-derived baselines will likely not show close agreement as the WG is unable to capture natural climate variability. Moreover, Harding et al (2014) argue that the interpolation of empirical weather data presents its own uncertainty and bias. We address uncertainty inherent in the WG by providing a range of probable scenarios for clay-subsidence hazard (Fig.…”

Spatial and temporal variability of the frost‐free season in Central Europe and its circulation background

“…For areas in the central and eastern parts of the continent, spring trends are more important, because of an increase in air temperature and the early‐commencing FFS (Scheifinger et al , ; Auer et al , ; Jylhä et al , ; Goergen et al , ; Potop et al , ; Ustrnul et al , ). Regions under the influence of maritime air masses and the southern part of the continent recorded more pronounced changes in autumn (Matzneller et al , ; Fernández‐Montes and Rodrigo, ; Prior and Perry, ; Harding et al , ). Similar trends were observed by Auer et al () in the higher parts of the Alps, which confirms the oceanic character of mountain climate.…”

Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

“…We follow [18] and use five agro-climate indices developed by [19] that were deemed 'very' or 'quite' useful by land management stakeholder focus groups. Not only do these metrics have little conceptual overlap, they also account for various stresses of land productivity and management practices.…”

“…While each of these climate indices provides important insight on agricultural responses under climate change, [10] show that there are significant variations in model performance depending on the choice of drought indices. More recently, studies have focused on agro-climate or agro-meteorological indices that are designed to assess the potential changes in crop exposure to temperature (heat and cold) and water stresses [16][17][18]. In addition, metrics relevant to management practices, such as start of field operations and growing season length, provide additional value to farmers and decision makers [19].…”