A simple and coherent framework for partitioning uncertainty in multimodel climate ensembles is presented. The analysis of variance (ANOVA) is used to decompose a measure of total variation ...additively into scenario uncertainty, model uncertainty, and internal variability. This approach requires fewer assumptions than existing methods and can be easily used to quantify uncertainty related to model–scenario interaction—the contribution to model uncertainty arising from the variation across scenarios of model deviations from the ensemble mean. Uncertainty in global mean surface air temperature is quantified as a function of lead time for a subset of the Coupled Model Intercomparison Project phase 3 ensemble and results largely agree with those published by other authors: scenario uncertainty dominates beyond 2050 and internal variability remains approximately constant over the twenty-first century. Both elements of model uncertainty, due to scenario-independent and scenario-dependent deviations from the ensemble mean, are found to increase with time. Estimates of model deviations that arise as by-products of the framework reveal significant differences between models that could lead to a deeper understanding of the sources of uncertainty in multimodel ensembles. For example, three models show a diverging pattern over the twenty-first century, while another model exhibits an unusually large variation among its scenario-dependent deviations.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
The United Nations Framework Convention on Climate Change (UNFCCC) process agreed in Paris to limit global surface temperature rise to “well below 2°C above pre-industrial levels.” But what ...period is preindustrial? Somewhat remarkably, this is not defined within the UNFCCC’s many agreements and protocols. Nor is it defined in the IPCC’s Fifth Assessment Report (AR5) in the evaluation of when particular temperature levels might be reached because no robust definition of the period exists. Here we discuss the important factors to consider when defining a preindustrial period, based on estimates of historical radiative forcings and the availability of climate observations. There is no perfect period, but we suggest that 1720–1800 is the most suitable choice when discussing global temperature limits. We then estimate the change in global average temperature since preindustrial using a range of approaches based on observations, radiative forcings, global climate model simulations, and proxy evidence. Our assessment is that this preindustrial period was likely 0.55°–0.80°C cooler than 1986–2005 and that 2015 was likely the first year in which global average temperature was more than 1°C above preindustrial levels. We provide some recommendations for how this assessment might be improved in the future and suggest that reframing temperature limits with a modern baseline would be inherently less uncertain and more policy relevant.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Sea ice plays a crucial role in the Earth's energy and water budget and has a substantial impact on local and remote atmospheric and oceanic circulations. Predictions of Arctic sea‐ice conditions a ...few months to a few years in advance could be of interest for stakeholders. This article presents a review of the potential sources of Arctic sea‐ice predictability on these time‐scales. Predictability mainly originates from persistence or advection of sea‐ice anomalies, interactions with the ocean and atmosphere and changes in radiative forcing. After estimating the inherent potential predictability limit with state‐of‐the‐art models, current sea‐ice forecast systems are described, together with their performance. Finally, some challenges and issues in sea‐ice forecasting are presented, along with suggestions for future research priorities.
Understanding how the emergence of the anthropogenic warming signal from the noise of internal variability translates to changes in extreme event occurrence is of crucial societal importance. By ...utilising simulations of cumulative carbon dioxide (CO2) emissions and temperature changes from eleven earth system models, we demonstrate that the inherently lower internal variability found at tropical latitudes results in large increases in the frequency of extreme daily temperatures (exceedances of the 99.9th percentile derived from pre-industrial climate simulations) occurring much earlier than for mid-to-high latitude regions. Most of the world's poorest people live at low latitudes, when considering 2010 GDP-PPP per capita; conversely the wealthiest population quintile disproportionately inhabit more variable mid-latitude climates. Consequently, the fraction of the global population in the lowest socio-economic quintile is exposed to substantially more frequent daily temperature extremes after much lower increases in both mean global warming and cumulative CO2 emissions.
The relative importance of anthropogenic aerosol in decadal variations of historical climate is uncertain, largely due to uncertainty in aerosol radiative forcing. We analyze a novel large ensemble ...of simulations with HadGEM3‐GC3.1 for 1850–2014, where anthropogenic aerosol and precursor emissions are scaled to sample a wide range of historical aerosol radiative forcing with present‐day values ranging from –0.38 to –1.50 Wm–2. Five ensemble members are run for each of five aerosol scaling factors. Decadal variations in surface temperatures are strongly sensitive to aerosol forcing, particularly between 1950 and 1980. Post‐1980, trends are dominated by greenhouse gas forcing, with much lower sensitivity to aerosol emission differences. Most realizations with aerosol forcing more negative than about –1 Wm–2 simulate stronger cooling trends in the mid‐20th century compared with observations, while the simulated warming post‐1980 always exceeds observed warming, likelydue to a warm bias in the transient climate response in HadGEM3‐GC3.1.
Plain Language Summary
Anthropogenic aerosols have an overall cooling effect on climate due to their interaction with incoming solar radiation and influence on cloud properties. Their emissions have offset some of the historical warming induced by increasing greenhouse gases. However, the magnitude of the cooling induced by anthropogenic aerosol remains poorly constrained. In this study, we use a state‐of‐the‐art climate model, HadGEM3‐GC3.1, driven by different levels of aerosol emissions. This experimental setup tests the sensitivity of simulated historical temperatures to the strength of aerosol forcing in a climate model, all other factors remaining equal. Our results show that the period from 1951 to 1980 is particularly sensitive to aerosol forcing, coinciding with a period of rapid increases in global aerosol emissions and observed cooling over many regions, while temperature trends from 1980 onwards are primarily driven by increases in greenhouse gas concentrations. The observed temperatures over 1951–1980 are best reproduced by simulations with lower aerosol emissions than the standard configuration, implying that this model responds too strongly to aerosol forcing. Concurrently, the simulated temperatures warm faster than observed temperatures from 1980 onwards, suggesting that this climate model also responds more strongly to greenhouse gas forcing than observations suggest.
Key Points
Simulations sampling aerosol forcing uncertainty suggest that aerosol forcing in the HadGEM3‐GC3.1 CMIP6 historical simulations is too large
Comparing two key periods (1950–1980 and 1981–2010) with observations suggests a positive bias in the transient climate response in HadGEM3
Most realizations with aerosol forcing more negative than about –1 Wm–2 cool too much in the mid‐20th century
Decadal Climate Variability and Predictability Cassou, Christophe; Kushnir, Yochanan; Hawkins, Ed ...
Bulletin of the American Meteorological Society,
03/2018, Letnik:
99, Številka:
3
Journal Article
Recenzirano
Odprti dostop
The study of Decadal Climate Variability (DCV) and Predictability is the interdisciplinary endeavor to characterize, understand, attribute, simulate, and predict the slow, multiyear variations of ...climate at global (e.g., the recent slowdown of global mean temperature rise in the early 2000s) and regional (e.g., decadal modulation of hurricane activity in the Atlantic, ongoing drought in California or in the Sahel in the 1970s–80s, etc.) scales. This study remains very challenging despite decades of research, extensive progress in climate system modeling, and improvements in the availability and coverage of a wide variety of observations. Considerable obstacles in applying this knowledge to actual predictions remain.
This short article is a succint review paper about DCV and predictability. Based on listed issues and priorities, it also proposes a unifying theme referred to as “drivers of teleconnectivity” as a backbone to address and structure the core DCV research challenge. This framework goes beyond a preoccupation with changes in the global mean temperature and directly addresses the regional impacts of external (natural and anthropogenic) climate forcing and internal climate interactions; it thus explicitly deals with the societal needs for region-specific climate information. Such a framework also enables the integration of efforts in a large international research community toward advancing the observation, characterization, understanding, and prediction of DCV. Recommendations to make progress are provided as part of the contribution of the CLIVAR “DCVP Research Focus” group.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated ...characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop–climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty.
Determining the time of emergence of climates altered from their natural state by anthropogenic influences can help inform the development of adaptation and mitigation strategies to climate change. ...Previous studies have examined the time of emergence of climate averages. However, at the global scale, the emergence of changes in extreme events, which have the greatest societal impacts, has not been investigated before. Based on state-of-the-art climate models, we show that temperature extremes generally emerge slightly later from their quasi-natural climate state than seasonal means, due to greater variability in extremes. Nevertheless, according to model evidence, both hot and cold extremes have already emerged across many areas. Remarkably, even precipitation extremes that have very large variability are projected to emerge in the coming decades in Northern Hemisphere winters associated with a wettening trend. Based on our findings we expect local temperature and precipitation extremes to already differ significantly from their previous quasi-natural state at many locations or to do so in the near future. Our findings have implications for climate impacts and detection and attribution studies assessing observed changes in regional climate extremes by showing whether they will likely find a fingerprint of anthropogenic climate change.
As global surface temperatures continue to rise, both the duration and the intensity of heat waves across most land areas are expected to increase. The 2022 European summer broke a number of ...temperature records where a new record daily maximum temperature of 40.3°C was reached on 19th July making it the hottest July heat wave event in the UK. This paper aims to detect and analyse historical heat wave events, particularly prior to 1927 and compare these with recent events, particularly, 2022, which featured four summer heat wave events in the UK. This allows us to understand how noteworthy historical extremes are in comparison to those in recent decades, to place modern events into historical context, and to extend the sample of extreme events. Summer heat wave events have been detected between 1878 and 2022 from long station data in the UK. Heat wave extent, duration, and intensity have been analysed to compare past heat waves to the recent 2022 heat waves. For each of the summer months at least one of the top 10 most intense events between 1878 and 2022 occurred in the earliest third of the dataset (before 1927) emphasising the value of analysing early heat events. In all detected events, the anomalous UK heat was part of large‐scale European extreme heat when examining 20th‐century reanalysis data, associated with a high‐pressure system. The 2022 July event resembles in pattern of warming and circulation some earlier events, for example, in 1925. While there is a clear trend in the monthly data and the overall frequency of anomalously hot days, heat wave activity on daily scales even in the period 1878 and 1926 is considerable and in some cases comparable to modern heat wave events in the UK. The most intense events detected led to societal impacts based on UK newspaper articles from the period including impacts on the agricultural sector, health impacts, and travel disruptions, broadly comparable to impacts from recent events.
In this article, we analyse how historically observed extreme heat events can provide useful case studies for extreme heat and make comparisons to recent heat wave events, particularly in 2022. While there is a clear warming trend in the monthly data, heat wave activity on daily scales between 1878 and 1926 is considerable and comparable in some cases to modern heat wave events in the UK, however, the July 2022 heat wave was the most intense to have occurred on record. Detected events show large‐scale European extreme heat in 20th‐century reanalysis data, associated with a high‐pressure system over parts of Europe. Monthly temperature data capture these detected events reasonably well, with above‐average monthly temperatures aligning with months in which at least one heat wave event occurred. The most intense events detected lead to noticeable societal impacts based on newspaper articles from the period.
PDF
