Estimating daily climatologies for climate indices derived from climate model data and observations

Abstract

Climate indices help to describe the past, present, and the future climate. They are usually closer related to possible impacts and are therefore more illustrative to users than simple climate means. Indices are often based on daily data series and thresholds. It is shown that the percentile-based thresholds are sensitive to the method of computation, and so are the climatological daily mean and the daily standard deviation, which are used for bias corrections of daily climate model data. Sample size issues of either the observed reference period or the model data lead to uncertainties in these estimations. A large number of past ensemble seasonal forecasts, called hindcasts, is used to explore these sampling uncertainties and to compare two different approaches. Based on a perfect model approach it is shown that a fitting approach can improve substantially the estimates of daily climatologies of percentile-based thresholds over land areas, as well as the mean and the variability. These improvements are relevant for bias removal in long-range forecasts or predictions of climate indices based on percentile thresholds. But also for climate change studies, the method shows potential for use.Key PointsMore robust estimates of daily climate characteristicsStatistical fitting approachBased on a perfect model approach

Predictive skill of climate indices compared to mean quantities in seasonal forecasts

“…The resulting forecast and reanalysis daily percentiles are further smoothed using local linear regression (LOESS: Cleveland and Devlin, ) to reduce sampling variability as proposed in Mahlstein et al . (). Based on the daily percentiles, counts of threshold exceedances and accumulated threshold departures are computed analogously to Eqs and .…”

“…Processing daily data of seasonal forecasts on the other hand is computationally more expensive and more delicate bias removal techniques are needed (e.g. Mahlstein et al ., ). These factors result in higher computational needs and a later delivery of operational forecasts.…”

Skill of Subseasonal Forecasts in Europe: Effect of Bias Correction and Downscaling Using Surface Observations

“…The mean debiasing method is a correction technique in which the lead time‐dependent ensemble mean bias between the forecasts and the observations is estimated using a local polynomial regression (LOESS) (Cleveland et al, ; Cleveland & Devlin, ). Mahlstein et al () have shown that daily fluctuations in the climatology can be smoothed by applying such a LOESS fit and correction factors can be estimated more robustly than with alternative methods. For temperature we applied an additive correction and for precipitation a multiplicative correction to avoid artificial generation of negative precipitation values.…”

Characterization of European wind speed variability using weather regimes

“…Daily anomalies for both SLP and 10-m wind speed were computed from the daily climatology of the respective dataset for the period 1981-2016. Anomalies were filtered with a LOESS polynomial regression with a degree of smoothing = 0.35 optimized to remove the annual cycle and smooth out the short-term variability of the climatological estimates (Mahlstein et al 2015). Before classifying the WRs, daily gridded SLP anomalies were weighted by the cosine of the latitude, in order to ensure equal area weighting at each grid point.…”