Abstract:The climate is an aggregate of the mean and variability of a range of meteorological variables, notably temperature (T) and precipitation (P). While the impacts of an increase in global mean surface temperature (GMST) are commonly quantified through changes in regional means and extreme value distributions, a concurrent shift in the shapes of the distributions of daily T and P is arguably equally important. Here, we employ a 30-member ensemble of coupled climate model simulations (CESM1 LENS) to consistently q… Show more
“…Whether variability changes matter for impacts needs to be assessed on a case-by-case basis. For example, changes in daily temperature variability can have a disproportionate effect on the tails and thus extreme events (Samset et al, 2019). However, there is a clear need to better validate model internal variability, as we found models to differ considerably in their magnitude of internal variability (consistent with Maher et al, 2020, andSchlunegger et al, 2020), a topic that has so far received less attention (Deser et al, 2018;Simpson et al, 2018).…”
<p>Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty, and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple Single-Model Initial-Condition Large Ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, we revisit the framework from Hawkins and Sutton (2009) for uncertainty partitioning for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. We also investigate forced changes in variability itself, something that is newly possible with SMILEs. The available SMILEs are shown to be a good representation of the CMIP5 model diversity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections.</p>
“…Whether variability changes matter for impacts needs to be assessed on a case-by-case basis. For example, changes in daily temperature variability can have a disproportionate effect on the tails and thus extreme events (Samset et al, 2019). However, there is a clear need to better validate model internal variability, as we found models to differ considerably in their magnitude of internal variability (consistent with Maher et al, 2020, andSchlunegger et al, 2020), a topic that has so far received less attention (Deser et al, 2018;Simpson et al, 2018).…”
<p>Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty, and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple Single-Model Initial-Condition Large Ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, we revisit the framework from Hawkins and Sutton (2009) for uncertainty partitioning for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. We also investigate forced changes in variability itself, something that is newly possible with SMILEs. The available SMILEs are shown to be a good representation of the CMIP5 model diversity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections.</p>
“…Building on methodology developed by Samset et al (2019), we quantify the PDFs of daily mean and maximum near-surface (2-meter) air temperature (SAT and SAT max ), precipitation (precip), minimum daily surface relative humidity (RH min ), and surface wind speed (wind). While other climatic and weather-related factors, such as soil moisture and snow, also influence wildfire regimes, we focus on the variables that pose the most direct risk factors and form the basis for the Canadian forest fire weather index (see below), which we use as an aggregate measure of the change in wildfire risk.…”
Recent years have seen unprecedented fire activity at high latitudes and knowledge of future wildfire risk is key for adaptation and risk management. Here we present a systematic characterization of the probability distributions (PDFs) of fire weather conditions, and how it arises from underlying meteorological drivers of change, in five boreal forest regions, for pre-industrial conditions and different global warming levels. Using initial condition ensembles from two global climate models to characterize regional variability, we quantify the PDFs of daily maximum surface air temperature (SATmax), precipitation, wind, and minimum relative humidity (RHmin), and their evolution with global temperature. The resulting aggregate change in fire risk is quantified using the Canadian Fire Weather Index (FWI). 
In all regions we find increases in both means and upper tails of the FWI distribution, and a widening suggesting increased variability. The main underlying drivers are the projected increase in mean daily SATmax and decline in RHmin, marked already at +1 and +2°C global warming. The largest changes occur in Canada, where we estimate a doubling of days with moderate-or-higher FWI between +1°C and +4°C global warming, and the smallest in Alaska. While both models exhibit the same general features of change with warming, differences in magnitude of the shifts exist, particularly for RHmin, where the bias compared to reanalysis is also largest. Given its importance for the FWI, RHmin evolution is identified as an area in need of further research. 
While occurrence and severity of wildfires ultimately depend also on factors such as ignition and fuel, we show how improved knowledge of meteorological conditions conducive to high wildfire risk, already changing across the high latitudes, can be used as a first indication of near-term changes. Our results confirm that continued global warming can rapidly push boreal forest regions into increasingly unfamiliar fire weather regimes.
“…Another important source of uncertainty not explicitly addressable within the CMIP context is parameter uncertainty. Even within a single model structure, some response uncertainty can result from varying model parameters in a perturbed-physics ensemble (Murphy et al, 2004;Sanderson et al, 2008). Such parameter uncertainty is sampled inherently but non-systematically through a set of different models, such as CMIP.…”
Abstract. Partitioning uncertainty in projections of future climate change
into contributions from internal variability, model response uncertainty
and emissions scenarios has historically relied on making assumptions about
forced changes in the mean and variability. With the advent of multiple
single-model initial-condition large ensembles (SMILEs), these assumptions
can be scrutinized, as they allow a more robust separation between sources
of uncertainty. Here, the framework from Hawkins and Sutton (2009) for
uncertainty partitioning is revisited for temperature and precipitation
projections using seven SMILEs and the Coupled Model Intercomparison Project CMIP5 and CMIP6 archives. The original approach is shown to work
well at global scales (potential method bias < 20 %), while at
local to regional scales such as British Isles temperature or Sahel
precipitation, there is a notable potential method bias (up to 50 %), and
more accurate partitioning of uncertainty is achieved through the use of
SMILEs. Whenever internal variability and forced changes therein are
important, the need to evaluate and improve the representation of
variability in models is evident. The available SMILEs are shown to be a
good representation of the CMIP5 model diversity in many situations, making
them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute
and relative model uncertainty than CMIP5, although part of this difference
can be reconciled with the higher average transient climate response in
CMIP6. This study demonstrates the added value of a collection of SMILEs for
quantifying and diagnosing uncertainty in climate projections.
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