Gabriele C. Hegerl

Human contribution to more-intense precipitation extremes

Extremes of weather and climate can have devastating effects on human society and the environment. Understanding past changes in the characteristics of such events, including recent increases in the intensity of heavy precipitation events over a large part of the Northern Hemisphere land area, is critical for reliable projections of future changes. Given that atmospheric water-holding capacity is expected to increase roughly exponentially with temperature—and that atmospheric water content is increasing in accord with this theoretical expectation—it has been suggested that human-influenced global warming may be partly responsible for increases in heavy precipitation. Because of the limited availability of daily observations, however, most previous studies have examined only the potential detectability of changes in extreme precipitation through model–model comparisons. Here we show that human-induced increases in greenhouse gases have contributed to the observed intensification of heavy precipitation events found over approximately two-thirds of data-covered parts of Northern Hemisphere land areas. These results are based on a comparison of observed and multi-model simulated changes in extreme precipitation over the latter half of the twentieth century analysed with an optimal fingerprinting technique. Changes in extreme precipitation projected by models, and thus the impacts of future changes in extreme precipitation, may be underestimated because models seem to underestimate the observed increase in heavy precipitation with warming.

Trends in intense precipitation in the climate record

Observed changes in intense precipitation (e.g., the frequency of very heavy precipitation or the upper 0.3% of daily precipitation events) have been analyzed for over half of the land area of the globe. These changes have been linked to changes in intense precipitation for three transient climate model simulations, all with greenhouse gas concentrations increasing during the twentieth and twenty-first centuries and doubling in the later part of the twenty-first century. It was found that both the empirical evidence from the period of instrumental observations and model projections of a greenhouse-enriched atmosphere indicate an increasing probability of intense precipitation events for many extratropical regions including the United States. Although there can be ambiguity as to the impact of more frequent heavy precipitation events, the thresholds of the definitions of these events were raised here, such that they are likely to be disruptive. Unfortunately, reliable assertions of very heavy and extreme precipitation changes are possible only for regions with dense networks due to the small radius of correlation for many intense precipitation events.

Annular Modes in the Extratropical Circulation. Part II: Trends

The authors exploit the remarkable similarity between recent climate trends and the structure of the ‘‘annular modes’’ in the month-to-month variability (as described in a companion paper) to partition the trends into components linearly congruent with and linearly independent of the annular modes. The index of the Northern Hemisphere (NH) annular mode, referred to as the Arctic Oscillation (AO), has exhibited a trend toward the high index polarity over the past few decades. The largest and most significant trends are observed during the ‘‘active season’’ for stratospheric planetary wave‐mean flow interaction, January‐ March (JFM), when fluctuations in the AO amplify with height into the lower stratosphere. For the periods of record considered, virtually all of the JFM geopotential height falls over the polar cap region and the strengthening of the subpolar westerlies from the surface to the lower stratosphere, ;50% of the JFM warming over the Eurasian continent, ;30% of the JFM warming over the NH as a whole, ;40% of the JFM stratospheric cooling over the polar cap region, and ;40% of the March total column ozone losses poleward of 408N are linearly congruent with month-to-month variations in the AO index. Summertime sea level pressure falls over the Arctic basin are suggestive of a year-round drift toward the positive polarity of the AO, but the evidence is less conclusive. Owing to the photochemical memory inherent in the ozone distribution, roughly half the ozone depletion during the NH summer months is linearly dependent on AO-related ozone losses incurred during the previous active season. Lower-tropospheric geopotential height falls over the Antarctic polar cap region are indicative of a drift toward the high index polarity of the Southern Hemisphere (SH) annular mode with no apparent seasonality. In contrast, the trend toward a cooling and strengthening of the SH stratospheric polar vortex peaks sharply during the stratosphere’s relatively short active season centered in November. The most pronounced SH ozone losses have occurred in September‐October, one or two months prior to this active season. In both hemispheres, positive feedbacks involving ozone destruction, cooling, and a weakening of the wave-driven meridional circulation may be contributing to a delayed breakdown of the polar vortex and enhanced ozone losses during spring.

Detection of human influence on twentieth-century precipitation trends

Human influence on climate has been detected in surface air temperature, sea level pressure, free atmospheric temperature, tropopause height and ocean heat content. Human-induced changes have not, however, previously been detected in precipitation at the global scale, partly because changes in precipitation in different regions cancel each other out and thereby reduce the strength of the global average signal. Models suggest that anthropogenic forcing should have caused a small increase in global mean precipitation and a latitudinal redistribution of precipitation, increasing precipitation at high latitudes, decreasing precipitation at sub-tropical latitudes, and possibly changing the distribution of precipitation within the tropics by shifting the position of the Intertropical Convergence Zone. Here we compare observed changes in land precipitation during the twentieth century averaged over latitudinal bands with changes simulated by fourteen climate models. We show that anthropogenic forcing has had a detectable influence on observed changes in average precipitation within latitudinal bands, and that these changes cannot be explained by internal climate variability or natural forcing. We estimate that anthropogenic forcing contributed significantly to observed increases in precipitation in the Northern Hemisphere mid-latitudes, drying in the Northern Hemisphere subtropics and tropics, and moistening in the Southern Hemisphere subtropics and deep tropics. The observed changes, which are larger than estimated from model simulations, may have already had significant effects on ecosystems, agriculture and human health in regions that are sensitive to changes in precipitation, such as the Sahel.

Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations

Abstract Temperature and precipitation extremes and their potential future changes are evaluated in an ensemble of global coupled climate models participating in the Intergovernmental Panel on Climate Change (IPCC) diagnostic exercise for the Fourth Assessment Report (AR4). Climate extremes are expressed in terms of 20-yr return values of annual extremes of near-surface temperature and 24-h precipitation amounts. The simulated changes in extremes are documented for years 2046–65 and 2081–2100 relative to 1981–2000 in experiments with the Special Report on Emissions Scenarios (SRES) B1, A1B, and A2 emission scenarios. Overall, the climate models simulate present-day warm extremes reasonably well on the global scale, as compared to estimates from reanalyses. The model discrepancies in simulating cold extremes are generally larger than those for warm extremes, especially in sea ice–covered areas. Simulated present-day precipitation extremes are plausible in the extratropics, but uncertainties in extreme prec...

Climate sensitivity constrained by temperature reconstructions over the past seven centuries.

There is a Brief Communications Arising (01 March 2007) associated with this documentThe magnitude and impact of future global warming depends on the sensitivity of the climate system to changes in greenhouse gas concentrations. The commonly accepted range for the equilibrium global mean temperature change in response to a doubling of the atmospheric carbon dioxide concentration, termed climate sensitivity, is 1.5–4.5 K (ref. 2). A number of observational studies, however, find a substantial probability of significantly higher sensitivities, yielding upper limits on climate sensitivity of 7.7 K to above 9 K (refs 3–8). Here we demonstrate that such observational estimates of climate sensitivity can be tightened if reconstructions of Northern Hemisphere temperature over the past several centuries are considered. We use large-ensemble energy balance modelling and simulate the temperature response to past solar, volcanic and greenhouse gas forcing to determine which climate sensitivities yield simulations that are in agreement with proxy reconstructions. After accounting for the uncertainty in reconstructions and estimates of past external forcing, we find an independent estimate of climate sensitivity that is very similar to those from instrumental data. If the latter are combined with the result from all proxy reconstructions, then the 5–95 per cent range shrinks to 1.5–6.2 K, thus substantially reducing the probability of very high climate sensitivity.

Detecting greenhouse-gas-induced climate change with an optimal fingerprint method

Abstract A strategy using statistically optimal fingerprints to detect anthropogenic climate change is outlined and applied to near-surface temperature trends. The components of this strategy include observations, information about natural climate variability, and a “guess pattern” representing the expected time–space pattern of anthropogenic climate change. The expected anthropogenic climate change is identified through projection of the observations onto an appropriate optimal fingerprint, yielding a scalar-detection variable. The statistically optimal fingerprint is obtained by weighting the components of the guess pattern (truncated to some small-dimensional space) toward low-noise directions. The null hypothesis that the observed climate change is part of natural climate variability is then tested. This strategy is applied to detecting a greenhouse-gas-induced climate change in the spatial pattern of near-surface temperature trends defined for time intervals of 15–30 years. The expected pattern of cl...

Detection of Human Influence on a New, Validated 1500-Year Temperature Reconstruction

Abstract Climate records over the last millennium place the twentieth-century warming in a longer historical context. Reconstructions of millennial temperatures show a wide range of variability, raising questions about the reliability of currently available reconstruction techniques and the uniqueness of late-twentieth-century warming. A calibration method is suggested that avoids the loss of low-frequency variance. A new reconstruction using this method shows substantial variability over the last 1500 yr. This record is consistent with independent temperature change estimates from borehole geothermal records, compared over the same spatial and temporal domain. The record is also broadly consistent with other recent reconstructions that attempt to fully recover low-frequency climate variability in their central estimate. High variability in reconstructions does not hamper the detection of greenhouse gas–induced climate change, since a substantial fraction of the variance in these reconstructions from the ...

Avoiding Inhomogeneity in Percentile-Based Indices of Temperature Extremes

Using a Monte Carlo simulation, it is demonstrated that percentile-based temperature indices computed for climate change detection and monitoring may contain artificial discontinuities at the beginning and end of the period that is used for calculating the percentiles (base period). This would make these exceedance frequency time series unsuitable for monitoring and detecting climate change. The problem occurs because the threshold calculated in the base period is affected by sampling error. On average, this error leads to overestimated exceedance rates outside the base period. A bootstrap resampling procedure is proposed to estimate exceedance frequencies during the base period. The procedure effectively removes the inhomogeneity.

Detectability of Anthropogenic Changes in Annual Temperature and Precipitation Extremes

This paper discusses a study of temperature and precipitation indices that may be suitable for the early detection of anthropogenic change in climatic extremes. Anthropogenic changes in daily minimum and maximum temperature and precipitation over land simulated with two different atmosphere‐ocean general circulation models are analyzed. The use of data from two models helps to assess which changes might be robust between models. Indices are calculated that scan the transition from mean to extreme climate events within a year. Projected changes in temperature extremes are significantly different from changes in seasonal means over a large fraction (39%‐66%) of model grid points. Therefore, the detection of changes in seasonal mean temperature cannot be substituted for the detection of changes in extremes. The estimated signal-to-noise ratio for changes in extreme temperature is nearly as large as for changes in mean temperature. Both models simulate extreme precipitation changes that are stronger than the corresponding changes in mean precipitation. Climate change patterns for precipitation are quite different between the models, but both models simulate stronger increases of precipitation for the wettest day of the year (4.1% and 8.8%, respectively, over land) than for annual mean precipitation (0% and 0.7%, respectively). A signal-to-noise analysis suggests that changes in moderately extreme precipitation should become more robustly detectable given model uncertainty than changes in mean precipitation.