Climate Model Uncertainty Research Articles

The global aerosol‐cloud first indirect effect estimated using MODIS, MERRA, and AeroCom

AbstractAerosol‐cloud interactions (ACI) represent a significant source of forcing uncertainty in global climate models (GCMs). Estimates of radiative forcing due to ACI in Fifth Assessment Report range from −0.5 to −2.5 W m−2. A portion of this uncertainty is related to the first indirect, or Twomey, effect whereby aerosols act as nuclei for cloud droplets to condense upon. At constant liquid water content this increases the number of cloud droplets (Nd) and thus increases the cloud albedo. In this study we use remote‐sensing estimates of Nd within stratocumulus regions in combination with state‐of‐the‐art aerosol reanalysis from Modern‐Era Retrospective Analysis for Research and Applications version 2 (MERRA2) to diagnose how aerosols affect Nd. As in previous studies, Nd is related to sulfate mass through a power law relationship. The slope of the log‐log relationship between Nd and SO4 in maritime stratocumulus is found to be 0.31, which is similar to the range of 0.2–0.8 from previous in situ studies and remote‐sensing studies in the pristine Southern Ocean. Using preindustrial emissions models, the change in Nd between preindustrial and present day is estimated. Nd is inferred to have more than tripled in some regions. Cloud properties from Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate the radiative forcing due to this change in Nd. The Twomey effect operating in isolation is estimated to create a radiative forcing of −0.97 ± 0.23 W m−2 relative to the preindustrial era.

Regional climate model and model output statistics method uncertainties and the effect of temperature and precipitation on future river discharges in Scandinavia

We analyze the importance of regional climate models (GCM-RCMs) and model output statistics (MOS) methods as uncertainty sources for future changes of various hydrological variables in Scandinavia. The Hydrological Predictions for the Environment (HYPE) model, driven with daily mean temperature and precipitation, is used to simulate changes in river discharges and other hydrological components from the present-day climate (1980–2009) to mid-21st century conditions (2041–2070). The results show that GCM-RCM differences explain most of the spread in the simulated changes in the annual mean cycle of river discharge. At seasonal level, MOS-method uncertainties are most important during the winter and spring, which is likely explained by the sensitivity of snow processes to the representation of daily variability in the MOS methods. To gain physical insights into the physical processes, the relative importance of changes to temperature or precipitation on changes in surface hydrology are also assessed. In most regions of Scandinavia, changes to temperature explain most of the changes in river discharge volumes and spring peaks. Precipitation changes only have a secondary role in modulating these changes. Again, these results are mostly explained by changes in snow processes in winter and increases in evapotranspiration in summer.

Effects of Aqueous-Phase and Photochemical Processing on Secondary Organic Aerosol Formation and Evolution in Beijing, China.

Secondary organic aerosol (SOA) constitutes a large fraction of OA, yet remains a source of significant uncertainties in climate models due to incomplete understanding of its formation mechanisms and evolutionary processes. Here we evaluated the effects of photochemical and aqueous-phase processing on SOA composition and oxidation degrees in three seasons in Beijing, China, using high-resolution aerosol mass spectrometer measurements along with positive matrix factorization. Our results show that aqueous-phase processing has a dominant impact on the formation of more oxidized SOA (MO-OOA), and the contribution of MO-OOA to OA increases substantially as a function of relative humidity or liquid water content. In contrast, photochemical processing plays a major role in the formation of less oxidized SOA (LO-OOA), as indicated by the strong correlations between LO-OOA and odd oxygen (Ox = O3 + NO2) during periods of photochemical production (R2 = 0.59-0.80). Higher oxygen-to-carbon ratios of SOA during periods with higher RH were also found indicating a major role of aqueous-phase processing in changing the oxidation degree of SOA in Beijing. Episodes analyses further highlight that LO-OOA plays a more important role during the early stage of the formation of autumn/winter haze episodes while MO-OOA is more significant during the later evolution period.

Climate Policies Under Climate Model Uncertainty: Max-Min and Min-Max Regret

Temperature responses and optimal climate policies depend crucially on the choice of a particular climate model. To illustrate, the temperature responses to given emission reduction paths implied by the climate modules of the well-known integrated assessments models DICE, FUND and PAGE are described and compared. A dummy temperature module based on the climate denialists’ view is added. Using a simple welfare-maximising growth model of the global economy, the sensitivity of the optimal carbon price, renewable energy subsidy and energy transition to each of these climate models is discussed. The paper then derives max-min, max-max and min-max regret policies to deal with this particular form of climate (model) uncertainty and with climate scepticism. The max-min or min-max regret climate policies rely on a non-sceptic view of global warming and lead to a substantial and moderate amount of caution, respectively. The max-max leads to no climate policies in line with the view of climate sceptics.

On the relationship between climate sensitivity and modelling uncertainty

Climate model projections are used to investigate the potential impacts of climate change on future weather, agriculture, water resources, human health, the global economy, etc. However, climate projections have a broad range of associated uncertainties, and it is a challenge to take account of these uncertainties in impact studies and risk assessments. Knowing which uncertainties matter and which may be reduced via scientific research or political decisions can help policy-makers in making informed decisions, scientists in focusing their resources, and businesses in building resilience to uncertainties that cannot be avoided. On the global scale, the present political resistance or ability to move from agreements to significant action provides the largest uncertainty in climate projections, followed by the uncertainty associated with climate modelling itself. Here, we show that climate sensitivity is a very important source of model uncertainty over large parts of the globe not only for temperature, but also for precipitation and wind projections. Because ‘climate sensitivity’ is a collective term that encompasses a wide range of feedback mechanisms in the climate system, we may not know for a long time whether models with high or low climate sensitivities are more relevant for the twenty-first century projections. Nevertheless, investigations of climate impacts cannot wait. Here we argue that it is physically and statistically unsound to mix climate model with high and low climate sensitivities, and that the subset chosen for any impact study should depend on the question one is trying to answer.

Large differences in regional precipitation change between a first and second 2 K of global warming.

For adaptation and mitigation planning, stakeholders need reliable information about regional precipitation changes under different emissions scenarios and for different time periods. A significant amount of current planning effort assumes that each K of global warming produces roughly the same regional climate change. Here using 25 climate models, we compare precipitation responses with three 2 K intervals of global ensemble mean warming: a fast and a slower route to a first 2 K above pre-industrial levels, and the end-of-century difference between high-emission and mitigation scenarios. We show that, although the two routes to a first 2 K give very similar precipitation changes, a second 2 K produces quite a different response. In particular, the balance of physical mechanisms responsible for climate model uncertainty is different for a first and a second 2 K of warming. The results are consistent with a significant influence from nonlinear physical mechanisms, but aerosol and land-use effects may be important regionally.

Sensitivity of soil carbon fractions and their specific stabilization mechanisms to extreme soil warming in a subarctic grassland.

Terrestrial carbon cycle feedbacks to global warming are major uncertainties in climate models. For in-depth understanding of changes in soil organic carbon (SOC) after soil warming, long-term responses of SOC stabilization mechanisms such as aggregation, organo-mineral interactions and chemical recalcitrance need to be addressed. This study investigated the effect of 6years of geothermal soil warming on different SOC fractions in an unmanaged grassland in Iceland. Along an extreme warming gradient of +0 to ~+40°C, we isolated five fractions of SOC that varied conceptually in turnover rate from active to passive in the following order: particulate organic matter (POM), dissolved organic carbon (DOC), SOC in sand and stable aggregates (SA), SOC in silt and clay (SC-rSOC) and resistant SOC (rSOC). Soil warming of 0.6°C increased bulk SOC by 22±43% (0-10cm soil layer) and 27±54% (20-30cm), while further warming led to exponential SOC depletion of up to 79±14% (0-10cm) and 74±8% (20-30) in the most warmed plots (~+40°C). Only the SA fraction was more sensitive than the bulk soil, with 93±6% (0-10cm) and 86±13% (20-30cm) SOC losses and the highest relative enrichment in 13 C as an indicator for the degree of decomposition (+1.6±1.5‰ in 0-10cm and +1.3±0.8‰ in 20-30cm). The SA fraction mass also declined along the warming gradient, while the SC fraction mass increased. This was explained by deactivation of aggregate-binding mechanisms. There was no difference between the responses of SC-rSOC (slow-cycling) and rSOC (passive) to warming, and 13 C enrichment in rSOC was equal to that in bulk soil. We concluded that the sensitivity of SOC to warming was not a function of age or chemical recalcitrance, but triggered by changes in biophysical stabilization mechanisms, such as aggregation.

Isotopic constraints on the role of hypohalous acids in sulfate aerosol formation in the remote marine boundary layer

Abstract. Sulfate is an important component of global atmospheric aerosol, and has partially compensated for greenhouse gas-induced warming during the industrial period. The magnitude of direct and indirect radiative forcing of aerosols since preindustrial times is a large uncertainty in climate models, which has been attributed largely to uncertainties in the preindustrial environment. Here, we report observations of the oxygen isotopic composition (Δ17O) of sulfate aerosol collected in the remote marine boundary layer (MBL) in spring and summer in order to evaluate sulfate production mechanisms in pristine-like environments. Model-aided analysis of the observations suggests that 33–50 % of sulfate in the MBL is formed via oxidation by hypohalous acids (HOX = HOBr + HOCl), a production mechanism typically excluded in large-scale models due to uncertainties in the reaction rates, which are due mainly to uncertainties in reactive halogen concentrations. Based on the estimated fraction of sulfate formed via HOX oxidation, we further estimate that daily-averaged HOX mixing ratios on the order of 0.01–0.1 parts per trillion (ppt = pmol/mol) in the remote MBL during spring and summer are sufficient to explain the observations.

Climate change impacts on groundwater hydrology – where are the main uncertainties and can they be reduced?

ABSTRACTThis paper assesses how various sources of uncertainty propagate through the uncertainty cascade from emission scenarios through climate models and hydrological models to impacts, with a particular focus on groundwater aspects from a number of coordinated studies in Denmark. Our results are similar to those from surface water studies showing that climate model uncertainty dominates the results for projections of climate change impacts on streamflow and groundwater heads. However, we found uncertainties related to geological conceptualization and hydrological model discretization to be dominant for projections of well field capture zones, while the climate model uncertainty here is of minor importance. How to reduce the uncertainties on climate change impact projections related to groundwater is discussed, with an emphasis on the potential for reducing climate model biases through the use of fully coupled climate–hydrology models.Editor D. Koutsoyiannis; Associate editor not assigned

Analysis of hydrological extremes at different hydro-climatic regimes under present and future conditions

We investigate simulated hydrological extremes (i.e., high and low flows) under the present and future climatic conditions for five river basins worldwide: the Ganges, Lena, Niger, Rhine, and Tagus. Future projections are based on five GCMs and four emission scenarios. We analyse results from the HYPE, mHM, SWIM, VIC and WaterGAP3 hydrological models calibrated and validated to simulate each river. The use of different impact models and future projections allows for an assessment of the uncertainty of future impacts. The analysis of extremes is conducted for four different time horizons: reference (1981–2010), early-century (2006–2035), mid-century (2036–2065) and end-century (2070–2099). In addition, Sen’s non-parametric estimator of slope is used to calculate the magnitude of trend in extremes, whose statistical significance is assessed by the Mann–Kendall test. Overall, the impact of climate change is more severe at the end of the century and particularly in dry regions. High flows are generally sensitive to changes in precipitation, however sensitivity varies between the basins. Finally, results show that conclusions in climate change impact studies can be highly influenced by uncertainty both in the climate and impact models, whilst the sensitivity to climate modelling uncertainty becoming greater than hydrological model uncertainty in the dry regions.

Contributing factors for drought in United States forest ecosystems under projected future climates and their uncertainty

Observations of increasing global forest die-off related to drought are leading to more questions about potential increases in drought occurrence, severity, and ecological consequence in the future. Dry soils and warm temperatures interact to affect trees during drought; so understanding shifting risks requires some understanding of changes in both temperature and precipitation. Unfortunately, strong precipitation uncertainties in climate models yield substantial uncertainty in projections of drought occurrence. We argue that disambiguation of drought effects into temperature and precipitation-mediated processes can alleviate some of the implied uncertainty. In particular, the disambiguation can clarify geographic diversity in forest sensitivity to multifarious drivers of drought and mortality, making more specific use of geographically diverse climate projections. Such a framework may provide forest managers with an easier heuristic in discerning geographically diverse adaptation options. Warming temperatures in the future mean three things with respect to drought in forests: (1) droughts, typically already unusually hot periods, will become hotter, (2) the drying capacity of the air, measured as the vapor pressure deficit (VPD) will become greater, and (3) a smaller fraction of precipitation will fall as snow. More hot-temperature extremes will be more stressful in a direct way to living tissue, and greater VPD will increase pressure gradients within trees, exacerbating the risk of hydraulic failure. Reduced storage in snowpacks reduces summer water availability in some places. Warmer temperatures do not directly cause drier soils, however. In a hydrologic sense, warmer temperatures do little to cause “drought” as defined by water balances. Instead, much of the future additional longwave energy flux is expected to cause warming rather than evaporating water. Precipitation variations, in contrast, affect water balances and moisture availability directly; so uncertainties in future precipitation generate uncertainty in drought occurrence and severity projections. Although specific projections in annual and seasonal precipitation are uncertain, changes in inter-storm spacing and precipitation type (snow vs. rain) have greater certainty and may have utility in improving spatial projections of drought as perceived by vegetation, a value not currently captured by simple temperature-driven evaporation projections. This review ties different types of future climate shifts to expected consequences for drought and potential influences on physiology, and then explains sources of uncertainty for consideration in future mortality projections. One intention is to provide guidance on partitioning of uncertainty in projections of forest stresses.

Making energy simulation easier for future climate – Synthesizing typical and extreme weather data sets out of regional climate models (RCMs)

Higher availability of future climate data sets, generated by regional climate models (RCMs) with fine temporal and spatial resolutions, improves and facilitates the impact assessment of climate change. Due to significant uncertainties in climate modeling, several climate scenarios should be considered in the impact assessment. This increases the number of simulations and size of data sets, complicating the assessment and decision making. This article suggests an easy-to-use method to decrease the number of simulations for the impact assessment of climate change in energy and building studies. The method is based on synthesizing three sets of weather data out of one or more RCMs: one typical and two extremes. The method aims at decreasing the number of weather data sets without losing the quality and details of the original future climate scenarios. The application of the method is assessed for an office building in Geneva and the residential building stock in Stockholm.Results show that using the synthesized data sets provides an accurate estimation of future conditions. Variations and uncertainties of future climate are represented by the synthesized data. In the case of synthesizing weather data using multiple climate scenarios, the number of simulations and the size of data sets are decreased enormously. Combining the typical and extreme data sets enables to have better probability distributions of future conditions, very similar to the original RCM data.

Climate Change and Maize Yield in Iowa.

Climate is changing across the world, including the major maize-growing state of Iowa in the USA. To maintain crop yields, farmers will need a suite of adaptation strategies, and choice of strategy will depend on how the local to regional climate is expected to change. Here we predict how maize yield might change through the 21st century as compared with late 20th century yields across Iowa, USA, a region representing ideal climate and soils for maize production that contributes substantially to the global maize economy. To account for climate model uncertainty, we drive a dynamic ecosystem model with output from six climate models and two future climate forcing scenarios. Despite a wide range in the predicted amount of warming and change to summer precipitation, all simulations predict a decrease in maize yields from late 20th century to middle and late 21st century ranging from 15% to 50%. Linear regression of all models predicts a 6% state-averaged yield decrease for every 1°C increase in warm season average air temperature. When the influence of moisture stress on crop growth is removed from the model, yield decreases either remain the same or are reduced, depending on predicted changes in warm season precipitation. Our results suggest that even if maize were to receive all the water it needed, under the strongest climate forcing scenario yields will decline by 10–20% by the end of the 21st century.

Integrating parameter uncertainty of a process-based model in assessments of climate change effects on forest productivity

The parameter uncertainty of process-based models has received little attention in climate change impact studies. This paper aims to integrate parameter uncertainty into simulations of climate change impacts on forest net primary productivity (NPP). We used either prior (uncalibrated) or posterior (calibrated using Bayesian calibration) parameter variations to express parameter uncertainty, and we assessed the effect of parameter uncertainty on projections of the process-based model 4C in Scots pine (Pinus sylvestris) stands under climate change. We compared the uncertainty induced by differences between climate models with the uncertainty induced by parameter variability and climate models together. The results show that the uncertainty of simulated changes in NPP induced by climate model and parameter uncertainty is substantially higher than the uncertainty of NPP changes induced by climate model uncertainty alone. That said, the direction of NPP change is mostly consistent between the simulations using the standard parameter setting of 4C and the majority of the simulations including parameter uncertainty. Climate change impact studies that do not consider parameter uncertainty may therefore be appropriate for projecting the direction of change, but not for quantifying the exact degree of change, especially if parameter combinations are selected that are particularly climate sensitive. We conclude that if a key objective in climate change impact research is to quantify uncertainty, parameter uncertainty as a major factor driving the degree of uncertainty of projections should be included.

Elemental composition and optical properties reveal changes in dissolved organic matter along a permafrost thaw chronosequence in a subarctic peatland

The fate of carbon stored in permafrost-zone peatlands represents a significant uncertainty in global climate modeling. Given that the breakdown of dissolved organic matter (DOM) is often a major pathway for decomposition in peatlands, knowledge of DOM reactivity under different permafrost regimes is critical for determining future climate feedbacks. To explore the effects of permafrost thaw and resultant plant succession on DOM reactivity, we used a combination of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), UV/Vis absorbance, and excitation-emission matrix spectroscopy (EEMS) to examine the DOM elemental composition and optical properties of 27 pore water samples gathered from various sites along a permafrost thaw sequence in Stordalen Mire, a thawing subarctic peatland in northern Sweden. The presence of dense Sphagnum moss, a feature that is dominant in the intermediate thaw stages, appeared to be the main driver of variation in DOM elemental composition and optical properties at Stordalen. Specifically, DOM from sites with Sphagnum had greater aromaticity, higher average molecular weights, and greater O/C, consistent with a higher abundance of phenolic compounds that likely inhibit decomposition. These compounds are released by Sphagnum and may accumulate due to inhibition of phenol oxidase activity by the acidic pH at these sites. In contrast, sites without Sphagnum, specifically fully-thawed rich fens, had more saturated, more reduced compounds, which were high in N and S. Optical properties at rich fens indicated the presence of microbially-derived DOM, consistent with the higher decomposition rates previously measured at these sites. These results indicate that Sphagnum acts as an inhibitor of rapid decomposition and CH4 release in thawing subarctic peatlands, consistent with lower rates of CO2 and CH4 production previously observed at these sites. However, this inhibitory effect may disappear if Sphagnum-dominated bogs transition to more waterlogged rich fens that contain very little to no living Sphagnum. Release of this inhibition allows for higher levels of microbial activity and potentially greater CH4 release, as has been observed in these fen sites.

A synthetic data set of high-spectral-resolution infrared spectra for the Arctic atmosphere

Abstract. Cloud microphysical and macrophysical properties are critical for understanding the role of clouds in climate. These properties are commonly retrieved from ground-based and satellite-based infrared remote sensing instruments. However, retrieval uncertainties are difficult to quantify without a standard for comparison. This is particularly true over the polar regions, where surface-based data for a cloud climatology are sparse, yet clouds represent a major source of uncertainty in weather and climate models. We describe a synthetic high-spectral-resolution infrared data set that is designed to facilitate validation and development of cloud retrieval algorithms for surface- and satellite-based remote sensing instruments. Since the data set is calculated using pre-defined cloudy atmospheres, the properties of the cloud and atmospheric state are known a priori. The atmospheric state used for the simulations is drawn from radiosonde measurements made at the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska (71.325° N, 156.615° W), a location that is generally representative of the western Arctic. The cloud properties for each simulation are selected from statistical distributions derived from past field measurements. Upwelling (at 60 km) and downwelling (at the surface) infrared spectra are simulated for 260 cloudy cases from 50 to 3000 cm−1 (3.3 to 200 µm) at monochromatic (line-by-line) resolution at a spacing of ∼ 0.01 cm−1 using the Line-by-line Radiative Transfer Model (LBLRTM) and the discrete-ordinate-method radiative transfer code (DISORT). These spectra are freely available for interested researchers from the NSF Arctic Data Center data repository (doi:10.5065/D61J97TT).

Climate models and hydrological parameter uncertainties in climate change impacts on monthly runoff and daily flow duration curve of a Mediterranean catchment

ABSTRACTClimate models and hydrological parameter uncertainties were quantified and compared while assessing climate change impacts on monthly runoff and daily flow duration curve (FDC) in a Mediterranean catchment. Simulations of the Soil and Water Assessment Tool (SWAT) model using an ensemble of behavioural parameter sets derived from the Generalized Likelihood Uncertainty Estimation (GLUE) method were approximated by feed-forward artificial neural networks (FF-NN). Then, outputs of climate models were used as inputs to the FF-NN models. Subsequently, projected changes in runoff and FDC were calculated and their associated uncertainty was partitioned into climate model and hydrological parameter uncertainties. Runoff and daily discharge of the Chiba catchment were expected to decrease in response to drier and warmer climatic conditions in the 2050s. For both hydrological indicators, uncertainty magnitude increased when moving from dry to wet periods. The decomposition of uncertainty demonstrated that climate model uncertainty dominated hydrological parameter uncertainty in wet periods, whereas in dry periods hydrological parametric uncertainty became more important.Editor M.C. Acreman; Associate editor S. Kanae

A new statistical approach to climate change detection and attribution

We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the “models are statistically indistinguishable from the truth” paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951–2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 $$\pm$$ 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings ( $$-0.01\pm 0.02$$ K).

Global observational diagnosis of soil moisture control on the land surface energy balance

AbstractAn understanding of where and how strongly the surface energy budget is constrained by soil moisture is hindered by a lack of large‐scale observations, and this contributes to uncertainty in climate models. Here we present a new approach combining satellite observations of land surface temperature and rainfall. We derive a Relative Warming Rate (RWR) diagnostic, which is a measure of how rapidly the land warms relative to the overlying atmosphere during 10 day dry spells. In our dry spell composites, 73% of the land surface between 60°S and 60°N warms faster than the atmosphere, indicating water‐stressed conditions, and increases in sensible heat. Higher RWRs are found for shorter vegetation and bare soil than for tall, deep‐rooted vegetation, due to differences in aerodynamic and hydrological properties. We show how the variation of RWR with antecedent rainfall helps to identify different evaporative regimes in the major nonpolar climate zones.

Risk Assessment of Climate Change Impacts on Runoff in Urmia Lake Basin, Iran

AbstractUrmia Lake, the largest lake in Iran, is an important water body and habitat for a variety of different species. Data of ten climate models among the atmosphere ocean general circulation models (AOGCMs) were assembled for the 2021–2050 period under A2 and B1 emission scenarios. First, both the 30-year historic (1961–1990) and 30-year future monthly temperature and precipitation in the region were generated and weighted by the beta function (β) that assigns weights to AOGCMs based on the model evaluations. Then, the cumulative density function (CDF) in each month was determined. Precipitation and temperature values at 25, 50, and 75% probability were extracted from the CDFs. Subsequently these values were introduced to the Long Ashton Research Station Weather Generator model (LARS-WG) to downscale and produce time series of temperature and precipitation in the future, subject to the uncertainty of climate models. Then, these monthly time series at different risk levels were introduced into the arti...