Abstract
Recent research has suggested that the overall dependence of convection near coasts on large-scale atmospheric conditions is weaker than over the open ocean or inland areas. This is due to the fact that in coastal regions convection is often supported by meso-scale land-sea interactions and the topography of coastal areas. As these effects are not resolved and not included in standard cumulus parametrization schemes, coastal convection is among the most poorly simulated phenomena in global models. To outline a possible parametrization framework for coastal convection we develop an idealized modeling approach and test its ability to capture the main characteristics of coastal convection. The new approach first develops a decision algorithm, or trigger function, for the existence of coastal convection. The function is then applied in a stochastic cloud model to increase the occurrence probability of deep convection when land-sea interactions are diagnosed to be important. The results suggest that the combination of the trigger function with a stochastic model is able to capture the occurrence of deep convection in atmospheric conditions often found for coastal convection. When coastal effects are deemed to be present the spatial and temporal organization of clouds that has been documented form observations is well captured by the model. The presented modeling approach has therefore potential to improve the representation of clouds and convection in global numerical weather forecasting and climate models.
| Original language | English |
|---|---|
| Pages (from-to) | 2561-2582 |
| Number of pages | 22 |
| Journal | Journal of Advances in Modeling Earth Systems |
| Volume | 9 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 1 Nov 2017 |
Keywords
- coastal convection
- cumulus parametrization
- land-sea interaction
- land-sea-breezes
- numerical models
- stochastic parametrization
Cite this
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Coastal Tropical Convection in a Stochastic Modeling Framework. / Bergemann, Martin; Khouider, Boualem; Jakob, Christian.
In: Journal of Advances in Modeling Earth Systems, Vol. 9, No. 7, 01.11.2017, p. 2561-2582.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Coastal Tropical Convection in a Stochastic Modeling Framework
AU - Bergemann, Martin
AU - Khouider, Boualem
AU - Jakob, Christian
PY - 2017/11/1
Y1 - 2017/11/1
N2 - Recent research has suggested that the overall dependence of convection near coasts on large-scale atmospheric conditions is weaker than over the open ocean or inland areas. This is due to the fact that in coastal regions convection is often supported by meso-scale land-sea interactions and the topography of coastal areas. As these effects are not resolved and not included in standard cumulus parametrization schemes, coastal convection is among the most poorly simulated phenomena in global models. To outline a possible parametrization framework for coastal convection we develop an idealized modeling approach and test its ability to capture the main characteristics of coastal convection. The new approach first develops a decision algorithm, or trigger function, for the existence of coastal convection. The function is then applied in a stochastic cloud model to increase the occurrence probability of deep convection when land-sea interactions are diagnosed to be important. The results suggest that the combination of the trigger function with a stochastic model is able to capture the occurrence of deep convection in atmospheric conditions often found for coastal convection. When coastal effects are deemed to be present the spatial and temporal organization of clouds that has been documented form observations is well captured by the model. The presented modeling approach has therefore potential to improve the representation of clouds and convection in global numerical weather forecasting and climate models.
AB - Recent research has suggested that the overall dependence of convection near coasts on large-scale atmospheric conditions is weaker than over the open ocean or inland areas. This is due to the fact that in coastal regions convection is often supported by meso-scale land-sea interactions and the topography of coastal areas. As these effects are not resolved and not included in standard cumulus parametrization schemes, coastal convection is among the most poorly simulated phenomena in global models. To outline a possible parametrization framework for coastal convection we develop an idealized modeling approach and test its ability to capture the main characteristics of coastal convection. The new approach first develops a decision algorithm, or trigger function, for the existence of coastal convection. The function is then applied in a stochastic cloud model to increase the occurrence probability of deep convection when land-sea interactions are diagnosed to be important. The results suggest that the combination of the trigger function with a stochastic model is able to capture the occurrence of deep convection in atmospheric conditions often found for coastal convection. When coastal effects are deemed to be present the spatial and temporal organization of clouds that has been documented form observations is well captured by the model. The presented modeling approach has therefore potential to improve the representation of clouds and convection in global numerical weather forecasting and climate models.
KW - coastal convection
KW - cumulus parametrization
KW - land-sea interaction
KW - land-sea-breezes
KW - numerical models
KW - stochastic parametrization
UR - http://www.scopus.com/inward/record.url?scp=85034102381&partnerID=8YFLogxK
U2 - 10.1002/2017MS001048
DO - 10.1002/2017MS001048
M3 - Article
VL - 9
SP - 2561
EP - 2582
JO - Journal of Advances in Modeling Earth Systems
JF - Journal of Advances in Modeling Earth Systems
SN - 1942-2466
IS - 7
ER -