scholarly journals Probabilistic Concepts in Intermediate-Complexity Climate Models: A Snapshot Attractor Picture

2015 ◽  
Vol 29 (1) ◽  
pp. 259-272 ◽  
Author(s):  
Mátyás Herein ◽  
János Márfy ◽  
Gábor Drótos ◽  
Tamás Tél
Keyword(s):  
Time Series ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Internal Variability ◽  
Co2 Concentration ◽  
Ensemble Average ◽  
Climate Projections ◽  
Intermediate Complexity ◽  
The North

Abstract A time series resulting from a single initial condition is shown to be insufficient for quantifying the internal variability in a climate model, and thus one is unable to make meaningful climate projections based on it. The authors argue that the natural distribution, obtained from an ensemble of trajectories differing solely in their initial conditions, of the snapshot attractor corresponding to a particular forcing scenario should be determined in order to quantify internal variability and to characterize any instantaneous state of the system in the future. Furthermore, as a simple measure of internal variability of any particular variable of the model, the authors suggest using its instantaneous ensemble standard deviation. These points are illustrated with the intermediate-complexity climate model Planet Simulator forced by a CO2 scenario, with a 40-member ensemble. In particular, the leveling off of the time dependence of any ensemble average is shown to provide a much clearer indication of reaching a steady state than any property of single time series. Shifts in ensemble averages are indicative of climate changes. The dynamical character of such changes is illustrated by hysteresis-like curves obtained by plotting the ensemble average surface temperature versus the CO2 concentration. The internal variability is found to be the most pronounced on small geographical scales. The traditionally used 30-yr temporal averages are shown to be considerably different from the corresponding ensemble averages. Finally, the North Atlantic Oscillation (NAO) index, related to the teleconnection paradigm, is also investigated. It is found that the NAO time series strongly differs in any individual realization from each other and from the ensemble average, and climatic trends can be extracted only from the latter.

scholarly journals Comparing climate time series – Part 2: A multivariate test

2021 ◽  
Vol 7 (2) ◽  
pp. 73-85
Author(s):  
Timothy DelSole ◽  
Michael K. Tippett
Keyword(s):  
Time Series ◽  
North Atlantic ◽  
Climate Models ◽  
Climate Model ◽  
Multivariate Time Series ◽  
Internal Variability ◽  
Var Model ◽  
Anthropogenic Aerosols ◽  
The North ◽  
The North Atlantic

Abstract. This paper proposes a criterion for deciding whether climate model simulations are consistent with observations. Importantly, the criterion accounts for correlations in both space and time. The basic idea is to fit each multivariate time series to a vector autoregressive (VAR) model and then test the hypothesis that the parameters of the two models are equal. In the special case of a first-order VAR model, the model is a linear inverse model (LIM) and the test constitutes a difference-in-LIM test. This test is applied to decide whether climate models generate realistic internal variability of annual mean North Atlantic sea surface temperature. Given the disputed origin of multidecadal variability in the North Atlantic (e.g., some studies argue it is forced by anthropogenic aerosols, while others argue it arises naturally from internal variability), the time series are filtered in two different ways appropriate to the two driving mechanisms. In either case, only a few climate models out of three dozen are found to generate internal variability consistent with observations. In fact, it is shown that climate models differ not only from observations, but also from each other, unless they come from the same modeling center. In addition to these discrepancies in internal variability, other studies show that models exhibit significant discrepancies with observations in terms of the response to external forcing. Taken together, these discrepancies imply that, at the present time, climate models do not provide a satisfactory explanation of observed variability in the North Atlantic.

Linking marine fog variability in Atlantic Canada to changes in large-scale atmospheric and marine features

2020 ◽  
Author(s):  
Patrick Duplessis ◽  
Minghong Zhang ◽  
William Perrie ◽  
George A Isaac ◽  
Rachel Y W Chang
Keyword(s):  
Large Scale ◽  
Climate Model ◽  
Internal Variability ◽  
Reanalysis Data ◽  
Sea Surface ◽  
Atlantic Canada ◽  
Climate Projections ◽  
The North ◽  
Marine Traffic ◽  
Highly Correlated

<p>Marine and coastal fog forms mainly from the cooling of warm and moist air advected over a colder sea surface. Atlantic Canada is one of the foggiest regions of the world due to the strong temperature contrast between the two oceanic currents in the vicinity. Recurring periods of low visibility notably disrupt off-shore operations and marine traffic, but also land and air transportation. On longer time-scales, marine fog variability also has a significant impact on the global radiative budget. Clouds, including fog, are the greatest source of uncertainty in the current climate projections because of their complex feedback mechanisms. Meteorological records indicate a significant negative trend in the occurrence of foggy conditions over the past six decades at most airports in Atlantic Canada, with large internal variability, including interannual and interdecadal variations. Using the airport observations, reanalysis data and climate model outputs, we investigated the various variabilities on the trend, at interannual and interdecadal scales, and attempted to address what caused these changes in fog frequency. Our results show that the strength and position of the North Atlantic Subtropical High as well as the sea-surface temperature of the cold and warm waters near Atlantic Canada were highly correlated with fog occurrence. We applied the derived fog indices on climate model outputs and projected the fog trends and variability in the different future climate scenarios. The results from this study will be compared with those obtained from other methods and the implications will be discussed.</p>

scholarly journals Sources of Uncertainty in Future Projections of the Carbon Cycle

2016 ◽  
Vol 29 (20) ◽  
pp. 7203-7213 ◽  
Author(s):  
Alan J. Hewitt ◽  
Ben B. B. Booth ◽  
Chris D. Jones ◽  
Eddy S. Robertson ◽  
Andy J. Wiltshire ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Carbon Fluxes ◽  
Global Ocean ◽  
Lead Times ◽  
Carbon Uptake ◽  
The North ◽  
Ocean Carbon

Abstract The inclusion of carbon cycle processes within CMIP5 Earth system models provides the opportunity to explore the relative importance of differences in scenario and climate model representation to future land and ocean carbon fluxes. A two-way analysis of variance (ANOVA) approach was used to quantify the variability owing to differences between scenarios and between climate models at different lead times. For global ocean carbon fluxes, the variance attributed to differences between representative concentration pathway scenarios exceeds the variance attributed to differences between climate models by around 2025, completely dominating by 2100. This contrasts with global land carbon fluxes, where the variance attributed to differences between climate models continues to dominate beyond 2100. This suggests that modeled processes that determine ocean fluxes are currently better constrained than those of land fluxes; thus, one can be more confident in linking different future socioeconomic pathways to consequences of ocean carbon uptake than for land carbon uptake. The contribution of internal variance is negligible for ocean fluxes and small for land fluxes, indicating that there is little dependence on the initial conditions. The apparent agreement in atmosphere–ocean carbon fluxes, globally, masks strong climate model differences at a regional level. The North Atlantic and Southern Ocean are key regions, where differences in modeled processes represent an important source of variability in projected regional fluxes.

scholarly journals Climate noise effect on uncertainty of hydrological extremes: numerical experiments with hydrological and climate models

2015 ◽  
Vol 369 ◽  
pp. 49-53 ◽  
Author(s):  
A. N. Gelfan ◽  
V. A. Semenov ◽  
Yu. G. Motovilov
Keyword(s):  
Numerical Experiments ◽  
Climate Models ◽  
Hydrological Model ◽  
Climate Model ◽  
Initial Conditions ◽  
Hydrological Regime ◽  
Internal Variability ◽  
Climate Impact ◽  
Noise Effect ◽  
Climate Noise

Abstract. An approach has been proposed to analyze the simulated hydrological extreme uncertainty related to the internal variability of the atmosphere ("climate noise"), which is inherent to the climate system and considered as the lowest level of uncertainty achievable in climate impact studies. To assess the climate noise effect, numerical experiments were made with climate model ECHAM5 and hydrological model ECOMAG. The case study was carried out to Northern Dvina River basin (catchment area is 360 000 km2), whose hydrological regime is characterised by extreme freshets during spring-summer snowmelt period. The climate noise was represented by ensemble ECHAM5 simulations (45 ensemble members) with identical historical boundary forcing and varying initial conditions. An ensemble of the ECHAM5-outputs for the period of 1979–2012 was used (after bias correction post-processing) as the hydrological model inputs, and the corresponding ensemble of 45 multi-year hydrographs was simulated. From this ensemble, we derived flood statistic uncertainty caused by the internal variability of the atmosphere.

scholarly journals Climate Model Assessment of Changes in Winter–Spring Streamflow Timing over North America

2018 ◽  
Vol 31 (14) ◽  
pp. 5581-5593 ◽  
Author(s):  
Jonghun Kam ◽  
Thomas R. Knutson ◽  
P. C. D. Milly
Keyword(s):  
United States ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Internal Variability ◽  
Global Climate Models ◽  
Anthropogenic Influence ◽  
Model Assessment ◽  
The North ◽  
Streamflow Timing

Over regions where snowmelt runoff substantially contributes to winter–spring streamflows, warming can accelerate snowmelt and reduce dry-season streamflows. However, conclusive detection of changes and attribution to anthropogenic forcing is hindered by the brevity of observational records, model uncertainty, and uncertainty concerning internal variability. In this study, the detection/attribution of changes in midlatitude North American winter–spring streamflow timing is examined using nine global climate models under multiple forcing scenarios. Robustness across models, start/end dates for trends, and assumptions about internal variability are evaluated. Marginal evidence for an emerging detectable anthropogenic influence (according to four or five of nine models) is found in the north-central United States, where winter–spring streamflows have been starting earlier. Weaker indications of detectable anthropogenic influence (three of nine models) are found in the mountainous western United States/southwestern Canada and in the extreme northeastern United States/Canadian Maritimes. In the former region, a recent shift toward later streamflows has rendered the full-record trend toward earlier streamflows only marginally significant, with possible implications for previously published climate change detection findings for streamflow timing in this region. In the latter region, no forced model shows as large a shift toward earlier streamflow timing as the detectable observed shift. In other (including warm, snow free) regions, observed trends are typically not detectable, although in the U.S. central plains we find detectable delays in streamflow, which are inconsistent with forced model experiments.

scholarly journals Evaluating climate model performance with various parameter sets using observations over the last centuries

2010 ◽  
Vol 6 (2) ◽  
pp. 711-765 ◽  
Author(s):  
M. F. Loutre ◽  
A. Mouchet ◽  
T. Fichefet ◽  
H. Goosse ◽  
H. Goelzer ◽  
...  
Keyword(s):  
North Atlantic ◽  
Climate Model ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Climate Projections ◽  
Freshwater Flux ◽  
The North ◽  
Climate Simulations ◽  
Wide Range ◽  
The North Atlantic

Abstract. Many sources of uncertainties limit the accuracy and precision of climate projections. Here, we focus on the parameter uncertainty, i.e. the imperfect knowledge of the values of many physical parameters in a climate model. We use LOVECLIM, a global three-dimensional Earth system model of intermediate complexity and vary several parameters within their range of uncertainty. Nine climatic parameter sets and three carbon cycle parameter sets are identified. They all yield present climate simulations coherent with observations and they cover a wide range of climate responses to doubled atmospheric CO2 concentration and freshwater flux in the North Atlantic sensitivity experiments. They also simulate a large range of atmospheric CO2 concentrations in response to prescribed emissions. Climate simulations of the last millennium are performed with the 27 combinations of these parameter sets. A special attention is given to the ability of LOVECLIM to reproduce the evolution of several climate variables over the last few decades, for which observations are available. The model response, even its ocean component, is strongly dominated by the model sensitivity to an increase in atmospheric CO2 concentration but much slightly by its sensitivity to freshwater flux in the North Atlantic. The whole set of parameter sets leads to a wide range of simulated climates. Although only some parameter sets yield simulations that reproduce the observed key variables of the climate system over the last decades, all of them could be used to characterise extreme climate projections.

Constraints on European temperature and rainfall changes from attributed changes

2020 ◽  
Author(s):  
Gabriele Hegerl ◽  
Andrew Ballinger ◽  
Sabine Undorf
Keyword(s):  
Greenhouse Gas ◽  
Temperature Change ◽  
Climate Model ◽  
Coupled Model ◽  
Internal Variability ◽  
Climate Projections ◽  
Near Surface ◽  
The North ◽  
Mitigation And Adaptation ◽  
The North Atlantic Oscillation

<p>Quantifying and reducing the uncertainty of climate projections will benefit both mitigation and adaptation decisions. Observed climate change provides evaluation of climate model simulated change, but the contribution by different external forcing factors needs to be reliably separated in order to use observational constraints. We revisit this ASK (for Allen et al., 2000; Stott and Kettleborough, 2002) approach to use attributed responses to greenhouse gas forcing to constrain future predictions.</p><p>We derive constraints on the projected near-surface summer temperature change over Europe as well as over three European subregions. The temperature responses to different external forcings (natural and greenhouse-gas (GHG) or combined anthropogenic) are estimated as the multi-model means of historical simulations from the Coupled Model Intercomparison Project 5 and incoming CMIP6, and the range of factors by which they can be scaled and still be consistent with observations since 1950 (E-OBS) given internal variability is calculated and applied to future RCP8.5 simulations.</p><p>Results show that both the response to GHG-only and to the combined anthropogenic (including aerosols etc.) forcing are detectable in the observed temperature change over Europe, and that the response over the Mediterranean region might be underestimated. Observed precipitation changes over Europe are also detected over some regions, although the confounding effects of the North Atlantic Oscillation need to be considered carefully. The results demonstrate the successful application of the ASK method for constraining projections of regional change over Europe.</p><p> </p>

scholarly journals Towards High Resolution Climate Reconstruction Using an Off-line Data Assimilation and COSMO-CLM 5.00 Model

2017 ◽  
Author(s):  
Bijan Fallah ◽  
Walter Acevedo ◽  
Emmanuele Russo ◽  
Nico Becker ◽  
Ulrich Cubasch
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Climate Models ◽  
Climate Model ◽  
Chaotic Behavior ◽  
Initial Conditions ◽  
Regional Climate ◽  
Internal Variability ◽  
Background Error ◽  
Geographical Regions

Abstract. Paleo-proxy observations have been recently used to constrain the climate models through data assimilation (DA). However, both DA and climate models are computationally very expensive. Moreover, in paleo-DA, the assimilation period is usually too long for a dynamical model to follow the previous analysis state and the chaotic behavior of the model becomes dominant. The majority of the recent paleoclimate studies using DA have performed low or intermediate resolution global simulations along with an off-line DA approach. In an off-line DA, the re-initialisation cycle is completely removed after the assimilation step. In this paper, we design a computationally affordable DA to assimilate yearly pseudo and real observations into an ensemble of COSMO-CLM high resolution regional climate model (RCM) simulations over Europe, where the ensemble members slightly differ in boundary and initial conditions. Within a perfect model experiment, the performance of the applied DA scheme is evaluated with respect to its sensitivity to the noise levels of pseudo-observations. It was observed that the injected bias in the pseudo-observations does linearly impact the DA skill. Such experiments can serve as a tool for selection of proxy records, which can potentially reduce the background error when they are assimilated in the model. Additionally, the sensibility of the COSMO-CLM to the boundary conditions is addressed. The geographical regions, where the model exhibits high internal variability are identified. Two sets of experiments are conducted by averaging the observations over summer and winter. The dependency of the DA skill to different seasons is investigated. Furthermore, the effect of the spurious correlations within the observation space is studied and the optimal correlation length, within which the observations are assumed to be correlated, is detected. Finally, the real yearly-averaged observations are assimilated into the RCM and the performance is evaluated against a gridded observation dataset. We conclude that the DA approach is a promising tool for creating high resolution yearly analysis quantities. The affordable DA method can be applied to efficiently improve the climate field reconstruction efforts by combining high resolution paleo-climate simulations and the available proxy observations.

scholarly journals Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Seshagiri Rao Kolusu ◽  
Christian Siderius ◽  
Martin C. Todd ◽  
Ajay Bhave ◽  
Declan Conway ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Investment Decisions ◽  
Climate Impacts ◽  
Future Climate ◽  
Eastern Africa ◽  
Climate Projections ◽  
Systematic Effect ◽  
Risk Profiles ◽  
Model Weighting

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

Cloud Properties over the North Slope of Alaska: Identifying the Prevailing Meteorological Regimes

2012 ◽  
Vol 25 (23) ◽  
pp. 8238-8258 ◽  
Author(s):  
Johannes Mülmenstädt ◽  
Dan Lubin ◽  
Lynn M. Russell ◽  
Andrew M. Vogelmann
Keyword(s):  
Time Series ◽  
Climate Model ◽  
North Slope ◽  
Test Cases ◽  
Synoptic Scale ◽  
Long Time Series ◽  
The North ◽  
Cloud Properties ◽  
North Slope Of Alaska ◽  
Long Time

Abstract Long time series of Arctic atmospheric measurements are assembled into meteorological categories that can serve as test cases for climate model evaluation. The meteorological categories are established by applying an objective k-means clustering algorithm to 11 years of standard surface-meteorological observations collected from 1 January 2000 to 31 December 2010 at the North Slope of Alaska (NSA) site of the U.S. Department of Energy Atmospheric Radiation Measurement Program (ARM). Four meteorological categories emerge. These meteorological categories constitute the first classification by meteorological regime of a long time series of Arctic meteorological conditions. The synoptic-scale patterns associated with each category, which include well-known synoptic features such as the Aleutian low and Beaufort Sea high, are used to explain the conditions at the NSA site. Cloud properties, which are not used as inputs to the k-means clustering, are found to differ significantly between the regimes and are also well explained by the synoptic-scale influences in each regime. Since the data available at the ARM NSA site include a wealth of cloud observations, this classification is well suited for model–observation comparison studies. Each category comprises an ensemble of test cases covering a representative range in variables describing atmospheric structure, moisture content, and cloud properties. This classification is offered as a complement to standard case-study evaluation of climate model parameterizations, in which models are compared against limited realizations of the Earth–atmosphere system (e.g., from detailed aircraft measurements).

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