Should we believe model predictions of future climate change? Knutti, Reto
Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences,
12/2008, Letnik:
366, Številka:
1885
Journal Article
Recenzirano
Predictions of future climate are based on elaborate numerical computer models. As computational capacity increases and better observations become available, one would expect the model predictions to ...become more reliable. However, are they really improving, and how do we know? This paper discusses how current climate models are evaluated, why and where scientists have confidence in their models, how uncertainty in predictions can be quantified, and why models often tend to converge on what we observe but not on what we predict. Furthermore, it outlines some strategies on how the climate modelling community may overcome some of the current deficiencies in the attempt to provide useful information to the public and policy-makers.
A few days with heavy rain contribute disproportionately to total precipitation, while many days with light drizzle contribute much less. What is not appreciated is just how asymmetric this ...distribution is in time, and the even more asymmetric nature of trends due to climate change. We diagnose the temporal asymmetry in models and observations. Half of annual precipitation falls in the wettest 12 days each year in the median across observing stations worldwide. Climate models project changes in precipitation that are more uneven than present‐day precipitation. In a scenario with high greenhouse‐gas emissions, one fifth of the projected increase in rain falls in the wettest 2 days of the year and 70% in the wettest 2 weeks. Adjusting modeled unevenness to match present‐day unevenness at stations, half of precipitation increase occurs in the wettest 6 days each year.
Plain Language Summary
Rain falls unevenly in time, which can lead to floods and droughts. It is widely known that precipitation is uneven, but it is difficult to quantify. Here we develop a measure for the unevenness of precipitation: the number of the wettest days each year in which half of the annual rain falls. We apply this to rain observed by gauges around the world. At all gauges combined, it takes only 12 days each year for half of the rain to fall. We also apply the measure to climate model simulations, with projections for the rest of the century. In the climate model simulations, the change in future rainfall is even more uneven than rainfall today: In a scenario with high greenhouse‐gas emissions, half of the increase in rainfall happens in the wettest 6 days each year. Rather than assuming more rain in general, society needs to take measures to deal with little change most of the time and a handful of events with much more rain.
Key Points
Precipitation falls unevenly in time: In the median of observing stations, half of annual precipitation falls in the wettest 12 days
In response to warming, unevenness increases in 97% of climate models
The increase in precipitation in response to warming occurs primarily during events often considered extreme
Recent coordinated efforts, in which numerous climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those ...multi-model ensembles sample initial condition, parameter as well as structural uncertainties in the model design, and they have prompted a variety of approaches to quantify uncertainty in future climate in a probabilistic way. This paper outlines the motivation for using multi-model ensembles, reviews the methodologies published so far and compares their results for regional temperature projections. The challenges in interpreting multi-model results, caused by the lack of verification of climate projections, the problem of model dependence, bias and tuning as well as the difficulty in making sense of an 'ensemble of opportunity', are discussed in detail.
Of the carbon dioxide that we emit, a substantial fraction remains in the atmosphere for thousands of years. Combined with the slow response of the climate system, this results in the global ...temperature increase resulting from CO₂ being nearly proportional to the total emitted amount of CO₂ since preindustrial times. This has a number of simple but far-reaching consequences that raise important questions for climate change mitigation, policy and ethics. Even if anthropogenic emissions of CO₂ were stopped, most of the realized climate change would persist for centuries and thus be irreversible on human timescales, yet standard economic thinking largely discounts these long-term intergenerational effects. Countries and generations to first order contribute to both past and future climate change in proportion to their total emissions. A global temperature target implies a CO₂ “budget” or “quota”, a finite amount of CO₂ that society is allowed to emit to stay below the target. Distributing that budget over time and between countries is an ethical challenge that our world has so far failed to address. Despite the simple relationship between CO₂ emissions and temperature, the consequences for climate policy and for sharing the responsibility of reducing global CO₂ emissions can only be drawn in combination with judgments about equity, fairness, the value of future generations and our attitude towards risk.
Climate models reproduce the observed surface warming better than one would expect given the uncertainties in radiative forcing, climate sensitivity and ocean heat uptake, suggesting that different ...models show similar warming for different reasons. It is shown that while climate sensitivity and radiative forcing are indeed correlated across the latest ensemble of models, eliminating this correlation would not strongly change the uncertainty range of long‐term temperature projections. However, since most models do not incorporate the aerosol indirect effects, model agreement with observations may be partly spurious. The incorporation of more detailed aerosol effects in future models could lead to inconsistencies between simulated and observed past warming, unless the effects are small or compensated by additional forcings. It is argued that parameter correlations across models are neither unexpected nor problematic if the models are interpreted as conditional on observations.
The collection of Earth system models available in the archive of phase 5 of CMIP (CMIP5) represents, at least to some degree, a sample of uncertainty of future climate evolution. The presence of ...duplicated code as well as shared forcing and validation data in the multiple models in the archive raises at least three potential problems: biases in the mean and variance, the overestimation of sample size, and the potential for spurious correlations to emerge in the archive because of model replication. Analytical evidence is presented to demonstrate that the distribution of models in the CMIP5 archive is not consistent with a random sample, and a weighting scheme is proposed to reduce some aspects of model codependency in the ensemble. A method is proposed for selecting diverse and skillful subsets of models in the archive, which could be used for impact studies in cases where physically consistent joint projections of multiple variables (and their temporal and spatial characteristics) are required.
Understanding changes in precipitation variability is essential for a complete explanation of the hydrologic cycle's response to warming and its impacts. While changes in mean and extreme ...precipitation have been studied intensively, precipitation variability has received less attention, despite its theoretical and practical importance. Here, we show that precipitation variability in most climate models increases over a majority of global land area in response to warming (66% of land has a robust increase in variability of seasonal-mean precipitation). Comparing recent decades to RCP8.5 projections for the end of the 21
century, we find that in the global, multi-model mean, precipitation variability increases 3-4% K
globally, 4-5% K
over land and 2-4% K
over ocean, and is remarkably robust on a range of timescales from daily to decadal. Precipitation variability increases by at least as much as mean precipitation and less than moisture and extreme precipitation for most models, regions, and timescales. We interpret this as being related to an increase in moisture which is partially mitigated by weakening circulation. We show that changes in observed daily variability in station data are consistent with increased variability.
Uncertainty in model projections of future climate change arises due to internal variability, multiple possible emission scenarios, and different model responses to anthropogenic forcing. To robustly ...quantify uncertainty in multi-model ensembles, inter-dependencies between models as well as a models ability to reproduce observations should be considered. Here, a model weighting approach, which accounts for both independence and performance, is applied to European temperature and precipitation projections from the CMIP5 archive. Two future periods representing mid- and end-of-century conditions driven by the high-emission scenario RCP8.5 are investigated. To inform the weighting, six diagnostics based on three observational estimates are used to also account for uncertainty in the observational record. Our findings show that weighting the ensemble can reduce the interquartile spread by more than 20% in some regions, increasing the reliability of projected changes. The mean temperature change is most notably impacted by the weighting in the Mediterranean, where it is found to be 0.35 °C higher than the unweighted mean in the end-of-century period. For precipitation the largest differences are found for Northern Europe, with a relative decrease in precipitation of 2.4% and 3.4% for the two future periods compared to the unweighted case. Based on a perfect model test, it is found that weighting the ensemble leads to an increase in the investigated skill score for temperature and precipitation while minimizing the probability of overfitting.
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