27 research outputs found
Improving permafrost physics in the coupled Canadian Land Surface Scheme (v.3.6.2) and Canadian Terrestrial Ecosystem Model (v.2.1) (CLASS-CTEM)
The Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model (CLASS-CTEM) together form the land surface component of the Canadian Earth System Model (CanESM). Here, we investigate the impact of changes to CLASS-CTEM that are designed to improve the simulation of permafrost physics. Overall, 18 tests were performed, including changing the model configuration (number and depth of ground layers, different soil permeable depth datasets, adding a surface moss layer), and investigating alternative parameterizations of soil hydrology, soil thermal conductivity, and snow properties. To evaluate these changes, CLASS-CTEM outputs were compared to 1570 active layer thickness (ALT) measurements from 97 observation sites that are part of the Global Terrestrial Network fo
Uniqueness of the compactly supported weak solutions of the relativistic Vlasov-Darwin system
We use optimal transportation techniques to show uniqueness of the compactly
supported weak solutions of the relativistic Vlasov-Darwin system. Our proof
extends the method used by Loeper in J. Math. Pures Appl. 86, 68-79 (2006) to
obtain uniqueness results for the Vlasov-Poisson system.Comment: AMS-LaTeX, 21 page
Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1
The Canadian Earth System Model version 5.0 (CanESM5.0), the most recent major version of the global climate model developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) at Environment and Climate Change Canada (ECCC), has been used extensively in climate research and for providing future climate projections in the context of climate services. Previous studies have shown that CanESM5.0 performs well compared to other models and have revealed several model biases. To address these biases, the CCCma has recently initiated the âAnalysis for Developmentâ (A4D) activity, a coordinated analysis activity in support of CanESM development. Here we describe the goals and organization of this effort and introduce two variants (âp1â and âp2â) of a new CanESM version, CanESM5.1, which features important improvements as a result of the A4D activity. These improvements include the elimination of spurious stratospheric temperature spikes and an improved simulation of tropospheric dust. Other climate aspects of the p1 variant of CanESM5.1 are similar to those of CanESM5.0, while the p2 variant of CanESM5.1 features reduced equilibrium climate sensitivity and improved El NiñoâSouthern Oscillation (ENSO) variability as a result of intentional tuning of the atmospheric component. The A4D activity has also led to the improved understanding of other notable CanESM5.0 and CanESM5.1 biases, including the overestimation of North Atlantic sea ice, a cold bias over sea ice, biases in the stratospheric circulation and a cold bias over the Himalayas. It provides a potential framework for the broader climate community to contribute to CanESM development, which will facilitate further model improvements and ultimately lead to improved climate change information.</p
Global Carbon Budget 2023
Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate
(GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based f CO2 products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and incomplete understanding
of the contemporary carbon cycle. All uncertainties are reported as ±1Ï. For the year 2022, EFOS increased by 0.9 % relative to 2021, with fossil emissions at 9.9 ± 0.5 Gt C yrâ1 (10.2 ± 0.5 Gt C yrâ1 when the cement carbonation sink is not included), and ELUC was 1.2 ± 0.7 Gt C yrâ1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1 ± 0.8 Gt C yrâ1 (40.7±3.2 Gt CO2 yrâ1). Also, for 2022, GATM was 4.6±0.2 Gt C yrâ1 (2.18±0.1 ppm yrâ1; ppm denotes parts per million), SOCEAN was 2.8 ± 0.4 Gt C yrâ1, and SLAND was 3.8 ± 0.8 Gt C yrâ1, with a BIM of â0.1 Gt C yrâ1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1 ± 0.1 ppm. Preliminary data for 2023 suggest an increase in EFOS relative to 2022 of +1.1 % (0.0 % to 2.1 %) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959â2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt C yrâ1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living-data update documents changes in methods and data sets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work
are available at https://doi.org/10.18160/GCP-2023 (Friedlingstein et al., 2023)
Current and emerging developments in subseasonal to decadal prediction
Weather and climate variations of subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales a valuable tool for decision makers. As such, there is a growing interest in the scientific, operational and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus remains broadly similar (e.g., on precipitation, surface and upper ocean temperatures and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal and externally-forced variability such as anthropogenic warming in forecasts also becomes important.
The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correct, calibration and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Prograame (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis
Tiling soil textures for terrestrial ecosystem modelling via clustering analysis: a case study with CLASS-CTEM (version 2.1)
We investigate the application of clustering algorithms to represent sub-grid
scale variability in soil texture for use in a global-scale terrestrial
ecosystem model. Our model, the coupled Canadian Land Surface Scheme â
Canadian Terrestrial Ecosystem Model (CLASS-CTEM), is typically implemented
at a coarse spatial resolution (approximately 2.â8°âĂâ2.â8°) due to its use as the land surface component of the
Canadian Earth System Model (CanESM). CLASS-CTEM can, however, be run with tiling of the land surface as
a means to represent sub-grid heterogeneity. We first determined that the
model was sensitive to tiling of the soil textures via an idealized test case
before attempting to cluster soil textures globally. To cluster
a high-resolution soil texture dataset onto our coarse model grid, we use two
linked algorithms â the Ordering Points to Identify the Clustering Structure
(OPTICS) algorithm (Ankerst et al., 1999; Daszykowski et al., 2002) and the
algorithm of Sander et al. (2003) â to provide tiles of representative soil
textures for use as CLASS-CTEM inputs. The clustering process results in, on
average, about three tiles per CLASS-CTEM grid cell with most cells having
four or less tiles. Results from CLASS-CTEM simulations conducted with the
tiled inputs (Cluster) versus those using a simple grid-mean soil texture
(Gridmean) show CLASS-CTEM, at least on a global scale, is relatively
insensitive to the tiled soil textures; however, differences can be large in
arid or peatland regions. The Cluster simulation has generally lower soil
moisture and lower overall vegetation productivity than the Gridmean
simulation except in arid regions where plant productivity increases. In
these dry regions, the influence of the tiling is stronger due to the general
state of vegetation moisture stress which allows a single tile, whose soil
texture retains more plant-available water, to yield much higher
productivity. Although the use of clustering analysis appears promising as
a means to represent sub-grid heterogeneity, soil textures appear to be
reasonably represented for global-scale simulations using a simple grid-mean
value
Canadian snow and sea ice: historical trends and projections
The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on
developing and applying state of the art observational data to advance
dynamical prediction, projections, and understanding of seasonal snow cover
and sea ice in Canada and the circumpolar Arctic. Here, we present an
assessment from the CanSISE Network on trends in the historical record of
snow cover (fraction, water equivalent) and sea ice (area, concentration,
type, and thickness) across Canada. We also assess projected changes in snow
cover and sea ice likely to occur by mid-century, as simulated by the Coupled
Model Intercomparison Project Phase 5 (CMIP5) suite of Earth system models.
The historical datasets show that the fraction of Canadian land and marine
areas covered by snow and ice is decreasing over time, with seasonal and
regional variability in the trends consistent with regional differences in
surface temperature trends. In particular, summer sea ice cover has decreased
significantly across nearly all Canadian marine regions, and the rate of
multi-year ice loss in the Beaufort Sea and Canadian Arctic Archipelago has
nearly doubled over the last 8 years. The multi-model consensus over the
2020â2050 period shows reductions in fall and spring snow cover fraction and
sea ice concentration of 5â10âŻ% per decade (or 15â30âŻ% in total), with
similar reductions in winter sea ice concentration in both Hudson Bay and
eastern Canadian waters. Peak pre-melt terrestrial snow water equivalent
reductions of up to 10âŻ% per decade (30âŻ% in total) are projected across
southern Canada
Predictable Variations of the Carbon Sinks and Atmospheric CO<sub>2</sub> Growth in a MultiâModel Framework
International audienceInterâannual to decadal variability in the strength of the land and ocean carbon sinks impede accurate predictions of yearâtoâyear atmospheric carbon dioxide (CO2) growth rate. Such information is crucial to verify the effectiveness of fossil fuel emissions reduction measures. Using a multiâmodel framework comprising prediction systems initialized by the observed state of the physical climate, we find a predictive skill for the global ocean carbon sink of up to 6 years for some models. Longer regional predictability horizons are found across single models. On land, a predictive skill of up to 2 years is primarily maintained in the tropics and extraâtropics enabled by the initialization of the physical climate. We further show that anomalies of atmospheric CO2 growth rate inferred from natural variations of the land and ocean carbon sinks are predictable at lead time of 2 years and the skill is limited by the land carbon sink predictability horizon