scholarly journals Skill in Simulating Australian Precipitation at the Tropical Edge*

2016 ◽  
Vol 29 (4) ◽  
pp. 1477-1496 ◽  
Author(s):  
Penelope Maher ◽  
Steven C. Sherwood
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Linear Independence ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Southern Annular Mode ◽  
Reanalysis Data ◽  
Hadley Cell ◽  
Model Errors ◽  
Skill Scores

Abstract Expansion of the tropics will likely affect subtropical precipitation, but observed and modeled precipitation trends disagree with each other. Moreover, the dynamic processes at the tropical edge and their interactions with precipitation are not well understood. This study assesses the skill of climate models to reproduce observed Australian precipitation variability at the tropical edge. A multivariate linear independence approach distinguishes between direct (causal) and indirect (circumstantial) precipitation drivers that facilitate clearer attribution of model errors and skill. This approach is applied to observed precipitation and ERA-Interim reanalysis data and a representative subset of four models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and their CMIP3 counterparts. The drivers considered are El Niño–Southern Oscillation, southern annular mode, Indian Ocean dipole, blocking, and four tropical edge metrics (position and intensity of the subtropical ridge and subtropical jet). These models are skillful in representing the covariability of drivers and their influence on precipitation. However, skill scores have not improved in the CMIP5 subset relative to CMIP3 in either respect. The Australian precipitation response to a poleward-located Hadley cell edge remains uncertain, as opposing drying and moistening mechanisms complicate the net response. Higher skill in simulating driver covariability is not consistently mirrored by higher precipitation skill. This provides further evidence that modeled precipitation does not respond correctly to large-scale flow patterns; further improvements in parameterized moist physics are needed before the subtropical precipitation responses can be fully trusted. The multivariate linear independence approach could be applied more widely for practical model evaluation.

scholarly journals A multi-year short-range hindcast experiment with CESM1 for evaluating climate model moist processes from diurnal to interannual timescales

2021 ◽  
Vol 14 (1) ◽  
pp. 73-90
Author(s):  
Hsi-Yen Ma ◽  
Chen Zhou ◽  
Yunyan Zhang ◽  
Stephen A. Klein ◽  
Mark D. Zelinka ◽  
...  
Keyword(s):  
Short Range ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Heat Fluxes ◽  
Model Errors ◽  
Moist Processes ◽  
The Mean ◽  
Mean State

Abstract. We present a multi-year short-range hindcast experiment and its experimental design for better evaluation of both the mean state and variability of atmospheric moist processes in climate models from diurnal to interannual timescales and facilitate model development. We used the Community Earth System Model version 1 as the base model and performed a suite of 3 d hindcasts initialized every day starting at 00:00 Z from 1997 to 2012. Three processes – the diurnal cycle of clouds during different cloud regimes over the central US, precipitation and diabatic heating associated with the Madden–Julian Oscillation (MJO), and the response of precipitation, surface radiative and heat fluxes, as well as zonal wind stress to sea surface temperature anomalies associated with the El Niño–Southern Oscillation – are evaluated as examples to demonstrate how one can better utilize simulations from this experiment to gain insights into model errors and their connection to physical parameterizations or large-scale state. This is achieved by comparing the hindcasts with corresponding long-term observations for periods based on different phenomena. These analyses can only be done through this multi-year hindcast approach to establish robust statistics of the processes under well-controlled large-scale environment because these phenomena are either a result of interannual climate variability or only happen a few times in a given year (e.g., MJO, or cloud regime types). Furthermore, comparison of hindcasts to the typical simulations in climate mode with the same model allows one to infer what portion of a model's climate error directly comes from fast errors in the parameterizations of moist processes. As demonstrated here, model biases in the mean state and variability associated with parameterized moist processes usually develop within a few days and manifest within weeks to affect the simulations of large-scale circulation and ultimately the climate mean state and variability. Therefore, model developers can achieve additional useful understanding of the underlying problems in model physics by conducting a multi-year hindcast experiment.

Downscaling Climate Information

2016 ◽  
Author(s):  
Rasmus Benestad
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Southern Oscillation ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Local Climate ◽  
Skill Scores ◽  
The Right

What are the local consequences of a global climate change? This question is important for proper handling of risks associated with weather and climate. It also tacitly assumes that there is a systematic link between conditions taking place on a global scale and local effects. It is the utilization of the dependency of local climate on the global picture that is the backbone of downscaling; however, it is perhaps easiest to explain the concept of downscaling in climate research if we start asking why it is necessary. Global climate models are our best tools for computing future temperature, wind, and precipitation (or other climatological variables), but their limitations do not let them calculate local details for these quantities. It is simply not adequate to interpolate from model results. However, the models are able to predict large-scale features, such as circulation patterns, El Niño Southern Oscillation (ENSO), and the global mean temperature. The local temperature and precipitation are nevertheless related to conditions taking place over a larger surrounding region as well as local geographical features (also true, in general, for variables connected to weather/climate). This, of course, also applies to other weather elements. Downscaling makes use of systematic dependencies between local conditions and large-scale ambient phenomena in addition to including information about the effect of the local geography on the local climate. The application of downscaling can involve several different approaches. This article will discuss various downscaling strategies and methods and will elaborate on their rationale, assumptions, strengths, and weaknesses. One important issue is the presence of spontaneous natural year-to-year variations that are not necessarily directly related to the global state, but are internally generated and superimposed on the long-term climate change. These variations typically involve phenomena such as ENSO, the North Atlantic Oscillation (NAO), and the Southeast Asian monsoon, which are nonlinear and non-deterministic. We cannot predict the exact evolution of non-deterministic natural variations beyond a short time horizon. It is possible nevertheless to estimate probabilities for their future state based, for instance, on projections with models run many times with slightly different set-up, and thereby to get some information about the likelihood of future outcomes. When it comes to downscaling and predicting regional and local climate, it is important to use many global climate model predictions. Another important point is to apply proper validation to make sure the models give skillful predictions. For some downscaling approaches such as regional climate models, there usually is a need for bias adjustment due to model imperfections. This means the downscaling doesn’t get the right answer for the right reason. Some of the explanations for the presence of biases in the results may be different parameterization schemes in the driving global and the nested regional models. A final underlying question is: What can we learn from downscaling? The context for the analysis is important, as downscaling is often used to find answers to some (implicit) question and can be a means of extracting most of the relevant information concerning the local climate. It is also important to include discussions about uncertainty, model skill or shortcomings, model validation, and skill scores.

scholarly journals A multi-year short-range hindcast experiment for evaluating climate model moist processes from diurnal to interannual time scales

2020 ◽  
Author(s):  
Hsi-Yen Ma ◽  
Chen Zhou ◽  
Yunyan Zhang ◽  
Stephen A. Klein ◽  
Mark D. Zelinka ◽  
...  
Keyword(s):  
Time Scales ◽  
Short Range ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Model Errors ◽  
Moist Processes ◽  
The Mean ◽  
Mean State

Abstract. We present a multi-year short-range hindcast experiment and its experiment procedure for better evaluating both the mean state and variability of atmospheric moist processes in climate models from diurnal to interannual time scales to facilitate model development. We use the Community Earth System Model version 1 as the based model and performed a suite of 3-day long hindcasts every day starting at 00Z from 1997 to 2012. Three processes – the diurnal cycle of clouds during different cloud regimes over the Central U.S., precipitation and diabatic heating associated with the Madden-Julian Oscillation propagation, and the response of moist processes to sea surface temperature anomalies associated with the El Niño-Southern Oscillation – are evaluated as examples to demonstrate how one can better utilize simulations from this experiment design to gain insights into model errors and their connection to physical parameterizations or large-scale state. This is achieved by comparing the hindcasts with corresponding long-term observations for periods based on different phenomena. These analyses can only be done through this multi-year hindcast approach to establish robust statistics of the processes under well-controlled large-scale environment. Furthermore, comparison of hindcasts to the typical simulations in climate mode with the same model allows one to infer what portion of a model’s climate error directly comes from fast errors in the parameterizations of moist processes. As demonstrated here, model biases in the mean state and variability associated parameterized moist processes usually develop within a few days, and manifest within weeks to affect the simulations of large-scale circulation and ultimately the climate mean state and variability. Therefore, model developers can achieve additional useful understanding of the underlying problems in model physics by conducting a multi-year hindcast experiment.

scholarly journals Links between Central West Western Australian Rainfall Variability and Large-Scale Climate Drivers

2013 ◽  
Vol 26 (7) ◽  
pp. 2222-2246 ◽  
Author(s):  
Alexandre O. Fierro ◽  
Lance M. Leslie
Keyword(s):  
Western Australia ◽  
Large Scale ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Winter Season ◽  
Principal Component ◽  
Southern Annular Mode ◽  
Reanalysis Data ◽  
Past Century ◽  
Drying Trend

Abstract Over the past century, and especially after the 1970s, rainfall observations show an increase (decrease) of the wet summer (winter) season rainfall over northwest (southwest) Western Australia. The rainfall in central west Western Australia (CWWA), however, has exhibited comparatively much weaker coastal trends, but a more prominent inland increase during the wet summer season. Analysis of seasonally averaged rainfall data from a group of stations, representative of both the coastal and inland regions of CWWA, revealed that rainfall trends during the 1958–2010 period in the wet months of November–April were primarily associated with El Niño–Southern Oscillation (ENSO), and with the southern annular mode (SAM) farther inland. During the wet months of May–October, the Indian Ocean dipole (IOD) showed the most robust relationships. Those results hold when the effects of ENSO or IOD are excluded, and were confirmed using a principal component analysis of sea surface temperature (SST) anomalies, rainfall wavelet analyses, and point-by-point correlations of rainfall with global SST anomaly fields. Although speculative, given their long-term averages, reanalysis data suggest that from 1958 to 2010 the increase in CWWA inland rainfall largely is attributable to an increasing cyclonic anomaly trend over CWWA, bringing onshore moist tropical flow to the Pilbara coast. During May–October, the flow anomaly exhibits a transition from an onshore to offshore flow regime in the 2001–10 decade, which is consistent with the observed weaker drying trend during this period.

scholarly journals On the relationship between large-scale climate modes and regional synoptic patterns that drive Victorian rainfall

2008 ◽  
Vol 5 (5) ◽  
pp. 2791-2815 ◽  
Author(s):  
D. Verdon-Kidd ◽  
A. S. Kiem
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Southern Oscillation ◽  
Southern Annular Mode ◽  
Global Climate Models ◽  
Phase Changes ◽  
Rainfall Forecasting ◽  
Climate Modes ◽  
Synoptic Patterns ◽  
Decadal Scale

Abstract. In this paper regional (synoptic) and large-scale climate drivers of rainfall are investigated for Victoria, Australia. A non-linear classification methodology known as self-organizing maps (SOM) is used to identify 20 key regional synoptic patterns, which are shown to capture a range of significant synoptic features known to influence the climate of the region. Rainfall distributions are assigned to each of the 20 patterns for nine rainfall stations located across Victoria, resulting in a clear distinction between wet and dry synoptic types at each station. The influence of large-scale climate modes on the frequency and timing of the regional synoptic patterns is also investigated. This analysis revealed that phase changes in the El Niño Southern Oscillation (ENSO), the Southern Annular Mode (SAM) and/or Indian Ocean Dipole (IOD) are associated with a shift in the relative frequency of wet and dry synoptic types. Importantly, these results highlight the potential to utilise the link between the regional synoptic patterns derived in this study and large-scale climate modes to improve rainfall forecasting for Victoria, both in the short- (i.e. seasonal) and long-term (i.e. decadal/multi-decadal scale). In addition, the regional and large-scale climate drivers identified in this study provide a benchmark by which the performance of Global Climate Models (GCMs) may be assessed.

scholarly journals Projected Changes in Late-Twenty-First-Century Tropical Cyclone Frequency in 13 Coupled Climate Models from Phase 5 of the Coupled Model Intercomparison Project

2013 ◽  
Vol 26 (24) ◽  
pp. 9946-9959 ◽  
Author(s):  
K. J. Tory ◽  
S. S. Chand ◽  
J. L. McBride ◽  
H. Ye ◽  
R. A. Dare
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Climate Models ◽  
Coupled Model ◽  
Reanalysis Data ◽  
First Century ◽  
Model Intercomparison ◽  
Intercomparison Project ◽  
Twenty First Century ◽  
Projected Changes

Abstract Changes in tropical cyclone (TC) frequency under anthropogenic climate change are examined for 13 global models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), using the Okubo–Weiss–Zeta parameter (OWZP) TC-detection method developed by the authors in earlier papers. The method detects large-scale conditions within which TCs form. It was developed and tuned in atmospheric reanalysis data and then applied without change to the climate models to ensure model and detector independence. Changes in TC frequency are determined by comparing TC detections in the CMIP5 historical runs (1970–2000) with high emission scenario (representative concentration pathway 8.5) future runs (2070–2100). A number of the models project increases in frequency of higher-latitude tropical cyclones in the late twenty-first century. Inspection reveals that these high-latitude systems were subtropical in origin and are thus eliminated from the analysis using an objective classification technique. TC detections in 8 of the 13 models reproduce observed TC formation numbers and geographic distributions reasonably well, with annual numbers within ±50% of observations. TC detections in the remaining five models are particularly low in number (10%–28% of observed). The eight models with a reasonable TC climatology all project decreases in global TC frequency varying between 7% and 28%. Large intermodel and interbasin variations in magnitude and sign are present, with the greatest variations in the Northern Hemisphere basins. These results are consistent with results from earlier-generation climate models and thus confirm the robustness of coupled model projections of globally reduced TC frequency.

Reproducibility by Climate Models of Cloud Radiative Forcing Associated with Tropical Convection

2012 ◽  
Vol 25 (4) ◽  
pp. 1247-1262 ◽  
Author(s):  
Hiroki Ichikawa ◽  
Hirohiko Masunaga ◽  
Yoko Tsushima ◽  
Hiroshi Kanzawa
Keyword(s):  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Deep Convection ◽  
Coupled Model ◽  
Vertical Motion ◽  
Reanalysis Data ◽  
Cloud Radiative Forcing ◽  
Tropical Oceans ◽  
Suppression Mechanism

Abstract In this study, cloud radiative forcing (CRF) associated with convective activity over tropical oceans is analyzed for monthly mean data from twentieth-century simulations of 18 climate models participating in phase 3 of the Coupled Model Intercomparison Project (CMIP3) in comparison with observational and reanalysis data. The analysis is focused on the warm oceanic regions with sea surface temperatures (SSTs) above 27°C to exclude the regions with cold SSTs typically covered by low stratus clouds. CRF is evaluated for different regimes sorted by pressure-coordinated vertical motion at 500 hPa (ω500) as an index of large-scale circulation. The warm oceanic regions cover the regime of vertical motion ranging from strong ascent to weak descent. The most notable feature found in this study is a systematic underestimation by most models of the ratio of longwave cloud radiative forcing (LWCRF) to shortwave cloud radiative forcing (SWCRF) over the weak vertical motion regime defined as −10 < ω500 < 20 hPa day−1. The underestimation of the ratio corresponds to the underestimation of LWCRF and the overestimation of SWCRF. Clouds in models seem to be lower in the amount of high clouds but more reflective than those in the observations in this regime. In the weak vertical motion regime, the lower free troposphere is dry. In the large-scale environment condition, the reproducibility of LWCRF is high in models adopting the scheme where the relative humidity–based suppression for deep convection occurrence is implemented. Models adopting the Zhang and McFarlane scheme show good performance without such a suppression mechanism.

Interannual Tasmanian Rainfall Variability Associated with Large-Scale Climate Modes

2009 ◽  
Vol 22 (16) ◽  
pp. 4383-4397 ◽  
Author(s):  
Khalia J. Hill ◽  
Agus Santoso ◽  
Matthew H. England
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Southern Annular Mode ◽  
Reanalysis Data ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Climate Modes ◽  
Interannual Rainfall Variability ◽  
East West

Abstract Interannual rainfall variability over Tasmania is examined using observations and reanalysis data. Tasmanian rainfall is dominated by an east–west gradient of mean rainfall and variability. The Pacific–South American mode (PSA), El Niño–Southern Oscillation (ENSO), and the southern annular mode (SAM) each show clear influences on the interannual variability of Tasmanian rainfall. Composites of rainfall during each phase of ENSO and the PSA suggest a notable islandwide influence of these climate modes on Tasmanian rainfall. In contrast, the positive phase of the SAM is associated with drier conditions over the west of the island. The PSA and the SAM project most prominently over the southwest of the island, whereas the ENSO signature is strongest in the north. Empirical orthogonal functions (EOFs) of rainfall over Tasmania show a leading mode (explaining 72% of total variance) of coherent islandwide in-phase anomalies with dominant periods of 2 and 5 yr. The second EOF accounts for ∼14% of total variation, characterized by out-of-phase east–west anomalies, which is likely a combination of all three modes. The EOF1 mode can be attributed to ENSO, the PSA, and to a lesser extent the SAM.

scholarly journals Representation of Modes of Variability in Six U.S. Climate Models

2020 ◽  
Vol 33 (17) ◽  
pp. 7591-7617 ◽  
Author(s):  
Clara Orbe ◽  
Luke Van Roekel ◽  
Ángel F. Adames ◽  
Amin Dezfuli ◽  
John Fasullo ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Climate Modeling ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Southern Annular Mode ◽  
Quasi Biennial Oscillation ◽  
Annular Mode ◽  
Modes Of Variability ◽  
The Pacific

AbstractWe compare the performance of several modes of variability across six U.S. climate modeling groups, with a focus on identifying robust improvements in recent models [including those participating in phase 6 of the Coupled Model Intercomparison Project (CMIP)] compared to previous versions. In particular, we examine the representation of the Madden–Julian oscillation (MJO), El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the quasi-biennial oscillation (QBO) in the tropical stratosphere, and the dominant modes of extratropical variability, including the southern annular mode (SAM), the northern annular mode (NAM) [and the closely related North Atlantic Oscillation (NAO)], and the Pacific–North American pattern (PNA). Where feasible, we explore the processes driving these improvements through the use of “intermediary” experiments that utilize model versions between CMIP3/5 and CMIP6 as well as targeted sensitivity experiments in which individual modeling parameters are altered. We find clear and systematic improvements in the MJO and QBO and in the teleconnection patterns associated with the PDO and ENSO. Some gains arise from better process representation, while others (e.g., the QBO) from higher resolution that allows for a greater range of interactions. Our results demonstrate that the incremental development processes in multiple climate model groups lead to more realistic simulations over time.

Differential Credibility of Climate Modes in CMIP6

2021 ◽  
pp. 1
Author(s):  
Jacob Coburn ◽  
S.C. Pryor
Keyword(s):  
Southern Oscillation ◽  
Probability Distributions ◽  
Critical Role ◽  
Regional Scale ◽  
Southern Annular Mode ◽  
Climate Projections ◽  
Annular Mode ◽  
Climate Modes ◽  
First Order ◽  
Skill Scores

AbstractThis work quantitatively evaluates the fidelity with which the Northern Annular Mode (NAM), Southern Annular Mode (SAM), Pacific-North American pattern (PNA), El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) and the first-order mode interactions are represented in Earth System Model (ESM) output from the CMIP6 archive. Several skill metrics are used as part of a differential credibility assessment (DCA) of both spatial and temporal characteristics of the modes across ESMs, ESM families and specific ESM realizations relative to ERA5. The spatial patterns and probability distributions are generally well represented but skill scores that measure the degree to which the frequencies of maximum variance are captured are consistently lower for most ESMs and climate modes. Substantial variability in skill scores manifests across realizations from individual ESMs for the PNA and oceanic modes. Further, the ESMs consistently overestimate the strength of the NAM-PNA first-order interaction and underestimate the NAM-AMO connection. These results suggest that the choice of ESM and ESM realizations will continue to play a critical role in determining climate projections at the global and regional scale at least in the near-term.

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