A Combined Optimization‐Assimilation Framework to Enhance the Predictive Skill of Community Land Model

Quantitatively identifying the influences of vegetation restoration (VR) on water resources is crucial to ecological planning. Although vegetation coverage has improved on the Loess Plateau (LP) of China since the implementation of VR policy, the way vegetation dynamics influences regional evapotranspiration (ET) remains controversial. In this study, we first investigate long-term spatiotemporal trends of total ET (TET) components, including ground evaporation (GE) and canopy ET (CET, sum of canopy interception and canopy transpiration) based on the GLEAM-ET dataset. The ET changes are attributed to VR on the LP from 2000 to 2015 and these results are quantitatively evaluated here using the Community Land Model (CLM). Finally, the relative contributions of VR and climate change to ET are identified by combining climate scenarios and VR scenarios. The results show that the positive effect of VR on CET is offset by the negative effect of VR on GE, which results in a weak variation in TET at an annual scale and an increased TET is only shown in summer. Regardless of the representative concentration pathway (RCP4.5 or RCP8.5), differences resulted from the responses of TET to different vegetation conditions ranging from −3.7 to −1.2 mm, while climate change from RCP4.5 to RCP8.5 caused an increase in TET ranging from 0.1 to 65.3 mm. These findings imply that climate change might play a dominant role in ET variability on the LP, and this work emphasizes the importance of comprehensively considering the interactions among climate factors to assess the relative contributions of VR and climate change to ET.

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Ensemble forecasting greatly expands the prediction horizon for ocean mesoscale variability

Communications Earth & Environment ◽

10.1038/s43247-021-00151-5 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Prasad G. Thoppil ◽

Sergey Frolov ◽

Clark D. Rowley ◽

Carolyn A. Reynolds ◽

Gregg A. Jacobs ◽

...

Keyword(s):

Initial Conditions ◽

Horizontal Resolution ◽

Ocean Model ◽

Mesoscale Dynamics ◽

Predictive Skill ◽

Freshwater Transport ◽

Prediction Horizon ◽

Initial Location ◽

Ocean Models ◽

High Uncertainty

AbstractMesoscale eddies dominate energetics of the ocean, modify mass, heat and freshwater transport and primary production in the upper ocean. However, the forecast skill horizon for ocean mesoscales in current operational models is shorter than 10 days: eddy-resolving ocean models, with horizontal resolution finer than 10 km in mid-latitudes, represent mesoscale dynamics, but mesoscale initial conditions are hard to constrain with available observations. Here we analyze a suite of ocean model simulations at high (1/25°) and lower (1/12.5°) resolution and compare with an ensemble of lower-resolution simulations. We show that the ensemble forecast significantly extends the predictability of the ocean mesoscales to between 20 and 40 days. We find that the lack of predictive skill in data assimilative deterministic ocean models is due to high uncertainty in the initial location and forecast of mesoscale features. Ensemble simulations account for this uncertainty and filter-out unconstrained scales. We suggest that advancements in ensemble analysis and forecasting should complement the current focus on high-resolution modeling of the ocean.

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Quantifying the Predictive Skill in Long-Range Forecasting. Part II: Model Error in Coarse-Grained Markov Models with Application to Ocean-Circulation Regimes

Journal of Climate ◽

10.1175/jcli-d-11-00110.1 ◽

2012 ◽

Vol 25 (6) ◽

pp. 1814-1826 ◽

Cited By ~ 19

Author(s):

Dimitrios Giannakis ◽

Andrew J. Majda

Keyword(s):

Long Range ◽

Ocean Circulation ◽

Climate Models ◽

Markov Models ◽

Low Frequency ◽

Model Error ◽

Ocean Model ◽

Coarse Grained ◽

Predictive Skill ◽

Relaxation To Equilibrium

Abstract An information-theoretic framework is developed to assess the predictive skill and model error in imperfect climate models for long-range forecasting. Here, of key importance is a climate equilibrium consistency test for detecting false predictive skill, as well as an analogous criterion describing model error during relaxation to equilibrium. Climate equilibrium consistency enforces the requirement that long-range forecasting models should reproduce the climatology of prediction observables with high fidelity. If a model meets both climate consistency and the analogous criterion describing model error during relaxation to equilibrium, then relative entropy can be used as an unbiased superensemble measure of the model’s skill in long-range coarse-grained forecasts. As an application, the authors investigate the error in modeling regime transitions in a 1.5-layer ocean model as a Markov process and identify models that are strongly persistent but their predictive skill is false. The general techniques developed here are also useful for estimating predictive skill with model error for Markov models of low-frequency atmospheric regimes.

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Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin

Monthly Weather Review ◽

10.1175/mwr-d-12-00098.1 ◽

2013 ◽

Vol 141 (3) ◽

pp. 1099-1117 ◽

Cited By ~ 18

Author(s):

Andrew Charles ◽

Bertrand Timbal ◽

Elodie Fernandez ◽

Harry Hendon

Keyword(s):

General Circulation ◽

Large Scale ◽

Seasonal Prediction ◽

General Circulation Models ◽

Meridional Wind ◽

Seasonal Forecasts ◽

Local Climate ◽

Continental Scale ◽

Predictive Skill ◽

Murray Darling Basin

Abstract Seasonal predictions based on coupled atmosphere–ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Large-scale fields from the Predictive Ocean–Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.

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A Scientific Function Test Framework for Modular Environmental Model Development: Application to the Community Land Model

2015 IEEE/ACM 1st International Workshop on Software Engineering for High Performance Computing in Science ◽

10.1109/se4hpcs.2015.10 ◽

2015 ◽

Cited By ~ 5

Author(s):

Dali Wang ◽

Tomislav Janjusic ◽

Colleen Iversen ◽

Peter Thornton ◽

Misha Karssovski ◽

...

Keyword(s):

Model Development ◽

Environmental Model ◽

Community Land Model ◽

Function Test ◽

Test Framework ◽

Development Application

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NAO predictability from external forcing in the late 20th century

npj Climate and Atmospheric Science ◽

10.1038/s41612-021-00177-8 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Jeremy M. Klavans ◽

Mark A. Cane ◽

Amy C. Clement ◽

Lisa N. Murphy

Keyword(s):

North Atlantic ◽

Radiative Forcing ◽

Climate Models ◽

North Atlantic Ocean ◽

Signal To Noise Ratio ◽

Prediction System ◽

Signal To Noise ◽

Late 20Th Century ◽

The North ◽

Predictive Skill

AbstractThe North Atlantic Oscillation (NAO) is predictable in climate models at near-decadal timescales. Predictive skill derives from ocean initialization, which can capture variability internal to the climate system, and from external radiative forcing. Herein, we show that predictive skill for the NAO in a very large uninitialized multi-model ensemble is commensurate with previously reported skill from a state-of-the-art initialized prediction system. The uninitialized ensemble and initialized prediction system produce similar levels of skill for northern European precipitation and North Atlantic SSTs. Identifying these predictable components becomes possible in a very large ensemble, confirming the erroneously low signal-to-noise ratio previously identified in both initialized and uninitialized climate models. Though the results here imply that external radiative forcing is a major source of predictive skill for the NAO, they also indicate that ocean initialization may be important for particular NAO events (the mid-1990s strong positive NAO), and, as previously suggested, in certain ocean regions such as the subpolar North Atlantic ocean. Overall, we suggest that improving climate models’ response to external radiative forcing may help resolve the known signal-to-noise error in climate models.

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Propagation of Thermohaline Anomalies and their predictive potential in the Northern North Atlantic

10.5194/egusphere-egu21-15092 ◽

2021 ◽

Author(s):

Helene R. Langehaug ◽

Pablo Ortega ◽

Francois Counillon ◽

Daniela Matei ◽

Elizabeth Maroon ◽

...

Keyword(s):

Time Scales ◽

North Atlantic ◽

Atlantic Water ◽

The Arctic ◽

Lead Times ◽

Band Pass Filter ◽

Pass Filter ◽

Predictive Skill ◽

Prediction Systems ◽

Water Pathway

<p>In this study we assess to what extent seven different dynamical prediction systems can retrospectively predict the winter sea surface temperature (SST) in the subpolar North Atlantic and the Nordic Seas in the time period 1970-2005. We focus in particular on the region where warm water flows poleward, i.e., the Atlantic water pathway, and on interannual-to-decadal time scales. To better understand why dynamical prediction systems have predictive skill or lack thereof, we confront them with a mechanism identified from observations &#8211; propagation of oceanic anomalies from low to high latitudes &#8211; on different forecast lead times. This observed mechanism shows that warm and cold anomalies propagate along the Atlantic water pathway within a certain time frame. A key result from this study is that most models have difficulty representing this mechanism, resulting in an overall poor prediction skill after 1-2 years lead times (after applying a band-pass filter to focus on interannual-to-decadal time scales). There is a link, although not very strong, between predictive skill and the representation of the SST propagation. Observational studies demonstrate predictability several years in advance in this region, thus suggesting a great potential for improvement of dynamical climate predictions by resolving the causes for the misrepresentation of the oceanic link. Inter model differences in simulating surface velocities along the Atlantic water pathway suggest that realistic velocities are important to better circulate anomalies poleward, and hence, increase predictive skill on interannual-to-decadal time scales in the oceanic gateway to the Arctic.</p>

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