Climate change, detection and indices

This document provides a report on aspects of the 2nd meeting of the Joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices

Multi-annual droughts in the English Lowlands: a review of their characteristics and climate drivers in the winter half-year

The English Lowlands is a relatively dry, densely populated region in the south-east of the UK in which water is used intensively. Consequently, parts of the region are water-stressed and face growing water resource pressures. The region is heavily dependent on groundwater and particularly vulnerable to long, multi-annual droughts primarily associated with dry winters. Despite this vulnerability, the atmospheric drivers of multi-annual droughts in the region are poorly understood, an obstacle to developing appropriate drought management strategies, including monitoring and early warning systems. To advance our understanding, we assess known key climate drivers in the winter half-year (October–March) and their likely relationships with multi-annual droughts in the region. We characterise historic multi-annual drought episodes back to 1910 for the English Lowlands using various meteorological and hydrological data sets. Multi-annual droughts are identified using a gridded precipitation series for the entire region, and refined using the Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI) and Standardized Groundwater level Index (SGI) applied to regional-scale river flow and groundwater time series. We explore linkages between a range of potential climatic driving factors and precipitation, river flow and groundwater level indicators in the English Lowlands for the winter half-year. The drivers or forcings include El Niño–Southern Oscillation (ENSO), the North Atlantic tripole sea surface temperature (SST) pattern, the Quasi-Biennial Oscillation (QBO), solar and volcanic forcing and the Atlantic Multi-decadal Oscillation (AMO). As expected, no single driver convincingly explains the occurrence of any multi-annual drought in the historical record. However, we demonstrate, for the first time, an association between La Niña episodes and winter rainfall deficits in some major multi-annual drought episodes in the English Lowlands. We also show significant (albeit relatively weak) links between ENSO and drought indicators applied to river flow and groundwater levels. We also show that some of the other drivers listed above are likely to influence English Lowlands rainfall. We conclude by signposting a direction for this future research effort

Design and analysis of climate model experiments for the efficient estimation of anthropogenic signals.

Presented herein is an experimental design that allows the effects of several radiative forcing factors on climate to be estimated as precisely as possible from a limited suite of atmosphere-only general circulation model (GCM) integrations. The forcings include the combined effect of observed changes in sea surface temperatures, sea ice extent, stratospheric (volcanic) aerosols, and solar output, plus the individual effects of several anthropogenic forcings. A single linear statistical model is used to estimate the forcing effects, each of which is represented by its global mean radiative forcing. The strong colinearity in time between the various anthropogenic forcings provides a technical problem that is overcome through the design of the experiment. This design uses every combination of anthropogenic forcing rather than having a few highly replicated ensembles, which is more commonly used in climate studies. Not only is this design highly efficient for a given number of integrations, but it also allows the estimation of (nonadditive) interactions between pairs of anthropogenic forcings. The simulated land surface air temperature changes since 1871 have been analyzed. The changes in natural and oceanic forcing, which itself contains some forcing from anthropogenic and natural influences, have the most influence. For the global mean, increasing greenhouse gases and the indirect aerosol effect had the largest anthropogenic effects. It was also found that an interaction between these two anthropogenic effects in the atmosphere-only GCM exists. This interaction is similar in magnitude to the individual effects of changing tropospheric and stratospheric ozone concentrations or to the direct (sulfate) aerosol effect. Various diagnostics are used to evaluate the fit of the statistical model. For the global mean, this shows that the land temperature response is proportional to the global mean radiative forcing, reinforcing the use of radiative forcing as a measure of climate change. The diagnostic tests also show that the linear model was suitable for analyses of land surface air temperature at each GCM grid point. Therefore, the linear model provides precise estimates of the space time signals for all forcing factors under consideration. For simulated 50-hPa temperatures, results show that tropospheric ozone increases have contributed to stratospheric cooling over the twentieth century almost as much as changes in well-mixed greenhouse gases

How potentially predictable is northern European winter climate a season ahead?

We estimate the potential predictability of European winter temperature using factors based on physical studies of their influences on European winter climate. These influences include sea surface temperature patterns in different oceans, major tropical volcanoes, the quasi-biennial oscillation in the tropical stratosphere, and anthropogenic climate change. We first assess the predictive skill for winter mean temperature in northern Europe by evaluating statistical hindcasts made using multiple regression models of temperature for Europe for winter and the January-February season. We follow this up by extending the methodology to all of Europe on a 5° × 5° grid and include rainfall for completeness. These results can form the basis of practical prediction methods. However, our main aim is to develop ideas to act as a benchmark for improving the performance of dynamical climate models. Because we consider only potential predictability, many of the predictors have estimated values coincident with the winter season being forecast. However, in each case, these values are predictable on average with considerable skill in advance of the winter season. A key conclusion is that to reproduce the results of this paper, dynamical forecasting models will require a fully resolved stratosphere

Revisiting the Earth's sea-level and energy budgets from 1961 to 2008

Using five ice core data sets combined into a single time series, we provide for the first time strong observational evidence for two distinct time scales of Arctic temperature fluctuation that are interpreted as variability associated with the Atlantic Multidecadal Oscillation (AMO). The dominant and the only statistically significant multidecadal signal has a time scale of about 20 years. The longer multidecadal variability of 45–85 years is not well defined and none of the time scales in this band is statistically significant. We compare these observed temperature fluctuations with results of coupled climate model simulations (HadCM3 and GFDL CM2.1). Both the 20–25 year and a variable longer AMO time scale are prominent in the models' long control runs. This periodicity supports our conjecture that the observed ice core fluctuations are a signature of the AMO. The robustness of this short time scale period in both observations and model simulations has implications for understanding the dominant AMO mechanisms in climate

Global surface air temperature variations during the twentieth century: Part 1, spatial, temporal and seasonal details

In Part 1, we review the uncertainties associated with combining land and marine instrumental records to produce regional-average series. The surface air temperature of the world has warmed 0.5°C since the middle of the nineteenth century. The warming in the northern Hemisphere only occurred in winter, spring and autumn. Summers are now no warmer than in the 1860s and 1870s. The same half-degree warming is seen in all seasons in the Southern Hemisphere. Spatial patterns of temperature anomalies during two warm decades, the 1930s and 1980s, all vary from season to season. Temperatures during the 1980s were by far the warmest in the last 140 years

Greenland ice core evidence for spatial and temporal variability of the Atlantic Multidecadal Oscillation

[1] The Greenland δ18O ice core record is used as a proxy for Greenland surface air temperatures and to interpret Atlantic Multidecadal Oscillation (AMO) variability. An analysis of annual δ18O data from six Arctic ice cores (five from Greenland and one from Canada's Ellesmere Island) suggests a significant AMO spatial and temporal variability within a recent period of 660 years. A dominant AMO periodicity near 20 years is clearly observed in the southern (Dye3 site) and the central (GISP2, Crete and Milcent) regions of Greenland. This 20-year variability is, however, significantly reduced in the northern (Camp Century and Agassiz Ice Cap) region, likely due to a larger distance from the Atlantic Ocean, and a much lower snow accumulation. A longer time scale AMO component of 45–65 years, which has been seen clearly in the 20th century SST data, is detected only in central Greenland ice cores. We find a significant difference between the AMO cycles during the Little Ice Age (LIA) and the Medieval Warm Period (MWP). The LIA was dominated by a ∼20 year AMO cycle with no other decadal or multidecadal scale variability above the noise level. However, during the preceding MWP the 20 year cycle was replaced by a longer scale cycle centered near a period of 43 years with a further 11.5 year periodicity. An analysis of two coupled atmosphere-ocean general circulation models control runs (UK Met Office HadCM3 and NOAA GFDL CM2.1) agree with the shorter and longer time-scales of Atlantic Meridional Overturning Circulation (AMOC) and temperature fluctuations with periodicities close to those observed. However, the geographic variability of these periodicities indicated by ice core data is not captured in model simulations

Greenland ice core evidence for spatial and temporal variability of the Atlantic Multidecadal Oscillation

[1] The Greenland δ18O ice core record is used as a proxy for Greenland surface air temperatures and to interpret Atlantic Multidecadal Oscillation (AMO) variability. An analysis of annual δ18O data from six Arctic ice cores (five from Greenland and one from Canada's Ellesmere Island) suggests a significant AMO spatial and temporal variability within a recent period of 660 years. A dominant AMO periodicity near 20 years is clearly observed in the southern (Dye3 site) and the central (GISP2, Crete and Milcent) regions of Greenland. This 20-year variability is, however, significantly reduced in the northern (Camp Century and Agassiz Ice Cap) region, likely due to a larger distance from the Atlantic Ocean, and a much lower snow accumulation. A longer time scale AMO component of 45–65 years, which has been seen clearly in the 20th century SST data, is detected only in central Greenland ice cores. We find a significant difference between the AMO cycles during the Little Ice Age (LIA) and the Medieval Warm Period (MWP). The LIA was dominated by a ∼20 year AMO cycle with no other decadal or multidecadal scale variability above the noise level. However, during the preceding MWP the 20 year cycle was replaced by a longer scale cycle centered near a period of 43 years with a further 11.5 year periodicity. An analysis of two coupled atmosphere-ocean general circulation models control runs (UK Met Office HadCM3 and NOAA GFDL CM2.1) agree with the shorter and longer time-scales of Atlantic Meridional Overturning Circulation (AMOC) and temperature fluctuations with periodicities close to those observed. However, the geographic variability of these periodicities indicated by ice core data is not captured in model simulations

High predictive skill of global surface temperature a year ahead

International audienceWe discuss 13 real-time forecasts of global annual-mean surface temperature issued by the United Kingdom Met Office for 1 year ahead for 2000-2012. These involve statistical, and since 2008, initialized dynamical forecasts using the Met Office DePreSys system. For the period when the statistical forecast system changed little, 2000-2010, issued forecasts had a high correlation of 0.74 with observations and a root mean square error of 0.07°C. However, the HadCRUT data sets against which issued forecasts were verified were biased slightly cold, especially from 2004, because of data gaps in the strongly warming Arctic. This observational cold bias was mainly responsible for a statistically significant warm bias in the 2000-2010 forecasts of 0.06°C. Climate forcing data sets used in the statistical method, and verification data, have recently been modified, increasing hindcast correlation skill to 0.80 with no significant bias. Dynamical hindcasts for 2000-2011 have a similar correlation skill of 0.78 and skillfully hindcast annual mean spatial global surface temperature patterns. Such skill indicates that we have a good understanding of the main factors influencing global mean surface temperature. Key Points High skill of predictions, 2000-2011 Even higher skill is potentially possible Coupled model hindcasts have at least this skill over same period ©2013. American Geophysical Union. All Rights Reserved