# Representation of Low-Level Clouds in West Central Africa in a convection-permitting regional climate simulation

Olivier Champagne, Vincent Moron, Nathalie Philippon, Cornelia Klein

► To cite this version:

Olivier Champagne, Vincent Moron, Nathalie Philippon, Cornelia Klein. Representation of Low-Level Clouds in West Central Africa in a convection-permitting regional climate simulation. EGU22, the 24th EGU General Assembly, May 2022, Vienna, Austria. 10.5194/egusphere-egu22-13520. hal-04544341

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Submitted on 12 Apr 2024

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<figure><img src="image_1.png" /></figure>
# Representation of Low-Level Clouds in West Central Africa in a convection-permitting regional climate simulation

Olivier Champagne, Vincent Moron, Nathalie Philippon, Cornelia Klein

► To cite this version:

Olivier Champagne, Vincent Moron, Nathalie Philippon, Cornelia Klein. Representation of Low-Level Clouds in West Central Africa in a convection-permitting regional climate simulation. EGU22, the 24th EGU General Assembly, May 2022, Vienne, Austria. 10.5194/egusphere-egu22-13520. hal-04544341

HAL Id: hal-04544341

https://hal.science/hal-04544341v1

Submitted on 12 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Representation of Low-Level Clouds in Western Central Africa in a convection-permitting regional climate simulation

Olivier Champagne¹, Nathalie Philippon¹, Vincent Moron², Cornelia Klein³

(1) Institut des Géosciences de l'Environnement, UGA, CNRS, IRD, Grenoble INP, Grenoble, France

(2) Aix-Marseille University, CNRS, IRD, INRAE, Collège de France CEREGE, Aix-en-Provence, France

(3) UK Centre for Ecology and Hydrology, Wallingford OX10 8BB, United Kingdom

EGU General assembly, Vienna, May 26 2022
# Representation of Low-Level Clouds in Western Central Africa in a convection-permitting regional climate simulation

Olivier Champagne¹, Nathalie Philippon¹, Vincent Moron², Cornelia Klein³

(1) Institut des Géosciences de l'Environnement, UGA, CNRS, IRD, Grenoble INP, Grenoble, France

(2) Aix-Marseille University, CNRS, IRD, INRAE, Collège de France CEREGE, Aix-en-Provence, France

(3) UK Centre for Ecology and Hydrology, Wallingford OX10 8BB, United Kingdom

EGU General assembly, Vienna, May 26 2022
Introduction

In Western Equatorial Africa (WEA), low level clouds (LLC) in the dry season (June-September) extremely important for the Forest.

Analyses of ground observations, satellites and ERA5 reanalyses in the DYVALOCCA project show:

> Cloudy days on the windward side of the low mountains range but strong afternoon clearing on the leeward side (Aellig et al. 2022, EGU, AS 1.10).

> "Cloudy" days favored by cold sea surface temperature (SSTs) over the near Atlantic Ocean and a warm El Niño. (Moron et al. 2022, submitted in JoC).

> Large deficiencies of CMIP6 in simulating LLC in WEA (Camberlin et al., 2022, submitted to Climate Dynamics), likely due to the parametrization of convection (Lucas-Picher, 2021).

CP4-Africa is a convection-permitting regional climate model (CPRCM), run in Africa in 1997-2006 (Stratton et al. 2018) but **no studies done on LCC in WEA**.

**Are low-level-clouds in WEA well represented in the CP4-Africa convection-permitting regional climate model?**

<figure><img src="image_2.png" /></figure>

West Equatorial Africa (Raffael Aellig, 2022)
Introduction

In Western Equatorial Africa (WEA), low level clouds (LLC) in the dry season (June-September) extremely important for the Forest.

Analyses of ground observations, satellites and ERA5 reanalyses in the DYVALOCCA project show:

> Cloudy days on the windward side of the low mountains range but strong afternoon clearing on the leeward side (Aellig et al. 2022, EGU, AS 1.10).

> "Cloudy" days favored by cold sea surface temperature (SSTs) over the near Atlantic Ocean and a warm El Niño. (Moron et al. 2022, submitted in JoC).

> Large deficiencies of CMIP6 in simulating LLC in WEA (Camberlin et al., 2022, submitted to Climate Dynamics), likely due to the parametrization of convection (Lucas-Picher, 2021).

CP4-Africa is a convection-permitting regional climate model (CPRCM), run in Africa in 1997-2006 (Stratton et al. 2018) but **no studies done on LCC in WEA**.

**Are low-level-clouds in WEA well represented in the CP4-Africa convection-permitting regional climate model?**

<figure><img src="image_2.png" /></figure>

West Equatorial Africa (Raffael Aellig, 2022)
**DATA: Low cloud cover from diverse sources:**

* CP4 Africa (1997-2006): (4km horizontal res. and reggrided to 25km) forced with N512L85 global model run in 1988-2010 and daily sea surface temperature (SST, Reynolds 1988)

* P25-Africa (1997-2006): (25km resolution) Regional climate model with same resolution and settings as the global model (convection parametrized). Aerosols and soil surface similar as CP4.

* Concatenated low level clouds in situ observations (1971-2019).

* Low cloud cover from ERA5 reanalyses data (1979-2019).

<figure><img src="image_3.png" /></figure>

**METHOD: Comparison of LLC dataset in term of:**

(1) The spatial variability of the diurnal cycle

(2) The temporal variability of these diurnal cycles (Diurnal types identified from in situ observations (points) using k-means algorithm).
**DATA: Low cloud cover from diverse sources:**

* CP4 Africa (1997-2006): (4km horizontal res. and reggrided to 25km) forced with N512L85 global model run in 1988-2010 and daily sea surface temperature (SST, Reynolds 1988)

* P25-Africa (1997-2006): (25km resolution) Regional climate model with same resolution and settings as the global model (convection parametrized). Aerosols and soil surface similar as CP4.

* Concatenated low level clouds in situ observations (1971-2019).

* Low cloud cover from ERA5 reanalyses data (1979-2019).

<figure><img src="image_3.png" /><figcaption>Low cloud fraction from diverse data sources</figcaption></figure>

**METHOD: Comparison of LLC dataset in term of:**

(1) The spatial variability of the diurnal cycle

(2) The temporal variability of these diurnal cycles (Diurnal types identified from in situ observations (points) using k-means algorithm).
# Diurnal change of low level clouds

<figure><img src="image_4.png" /></figure>

**In NE Republic of Congo:**

- All products fail to recreate **OBS** diurnal cycle (convective clouds counted as LCC in **ERA5** and **CP4?**)

- **CP4** improves **P25** negative bias

**In Eastern Gabon:**

- Better representation of diurnal cycle in **all models**

- **CP4** improves **P25** negative bias

- **CP4** still underestimate LCC at night

Average low-level cloud fraction

## Is CP4 improving representation of LCC?

Less underestimation of Low cloud cover in the east
BUT the diurnal cycle is still wrong (convective clouds?) and not
enough amplitude in the west (too low night clouds)

**Coastal Gabon:**

- **CP4** does not improve the overall **P25** negative bias

- **CP4** underestimate the diurnal amplitude

- **P25** still overestimate the diurnal amplitude
# Diurnal change of low level clouds

<figure><img src="image_4.png" /></figure>

**In NE Republic of Congo:**

- All products fail to recreate **OBS** diurnal cycle (convective clouds counted as LCC in **ERA5** and **CP4?**)

- **CP4** improves **P25** negative bias

**In Eastern Gabon:**

- Better representation of diurnal cycle in **all models**

- **CP4** improves **P25** negative bias

- **CP4** still underestimate LCC at night

Average low-level cloud fraction

## Is CP4 improving representation of LCC?

* Less underestimation of Low cloud cover in the east
* BUT the diurnal cycle is still wrong (convective clouds?) and not enough amplitude in the west (too low night clouds)

**Coastal Gabon:**

- **CP4** does not improve the overall **P25** negative bias

- **CP4** underestimate the diurnal amplitude

- **P25** still overestimate the diurnal amplitude
Diurnal types of low level clouds

The amplitude of diurnal types is underestimated in CP4, overestimated in P25

**In the East: Bright type**
more hegemonic in all products and mainly in P25

**In coastal Gabon: Cloudy**
stays dominant but **cloudy**
**night** is also present

Main differences is between OBS
and ERA5/CP4/P25
*(more in my online presentation)*

<figure><img src="image_5.png" /></figure>

Diurnal types
calculated
from OBS

- In the NE RC Type 'Bright' dominant,

- In Eastern Gabon mix between Type 'Cloudy night' and 'Cloudy'

- In coastal Gabon Cloudy is dominant
Diurnal types of low level clouds

<figure><img src="image_5.png" /></figure>

The amplitude of diurnal types is underestimated in CP4, overestimated in P25

**In the East: Bright type**
more hegemonic in all products and mainly in P25

**In coastal Gabon: Cloudy**
stays dominant but **cloudy**
**night** is also present

Diurnal types
calculated
from OBS

- In the NE RC Type 'Bright' dominant,

- In Eastern Gabon mix between Type 'Cloudy night' and 'Cloudy'

- In coastal Gabon Cloudy is dominant

Main differences is between OBS
and ERA5/CP4/P25
(more in my online presentation)
Interannual evolution of Diurnal types occurences

<figure><img src="image_6.png" /></figure>

Low SST in the Atlantic ocean decrease (increase) the number of cloudy night (cloudy) types in Gabon (Ex. 1997, 2001, 2005). Signal very strong in 1997 in P25 (El Nino?)
Interannual evolution of Diurnal types occurences

<figure><img src="image_6.png" /></figure>

Low SST in the Atlantic ocean decrease (increase) the number of cloudy night (cloudy) types in Gabon (Ex. 1997, 2001, 2005). Signal very strong in 1997 in P25 (El Nino?)
* The low-level clouds in West Equatorial Africa in June-september (1997-2006) from the convection permitting model CP4 Africa shows:

* ✓ A reduction of the underestimation of low-level clouds seen in P25 in the east (due to P25 simulating full LCC OR no LCC).

* X But a Diurnal cycle of low-level clouds still not matching well the ground observations: mainly in the North-east part of the region:

* ➤ Not enough low level clouds at night (difficulties simulating full cloudy skies in CP4)

* ➤ Too much clouds during the day in the North-east part of the region likely due to cumuliform clouds.

The apparent improvement of CP4 from P25 is more due to a more realistic diurnal cycle of convection than a real improvement in the representation of stratiform clouds.

* ➤ For future research: Medium/high-level clouds and other variables (temperature, wind, pressure, humidity...) should be investigated... and more on interannual variability!
* The low-level clouds in West Equatorial Africa in June-september (1997-2006) from the convection permitting model CP4 Africa shows:

* ✓ A reduction of the underestimation of low-level clouds seen in P25 in the east (due to P25 simulating full LCC OR no LCC).

* X But a Diurnal cycle of low-level clouds still not matching well the ground observations: mainly in the North-east part of the region:

* ➤ Not enough low level clouds at night (difficulties simulating full cloudy skies in CP4)

* ➤ Too much clouds during the day in the North-east part of the region likely due to cumuliform clouds.

The apparent improvement of CP4 from P25 is more due to a more realistic diurnal cycle of convection than a real improvement in the representation of stratiform clouds.

* ➤ For future research: Medium/high-level clouds and other variables (temperature, wind, pressure, humidity...) should be investigated... and more on interannual variability!
Thank you!

(Full presentation available online)

Contact: Olivier.champagne@univ-grenoble-alpes.fr
Thank you!

(Full presentation available online)

Contact: Olivier.champagne@univ-grenoble-alpes.fr